Exposure of Cultural Resources to 21st-Century Climate Change: Towards a risk management plan

From ScienceDirect

JorieClarkaJeremy S.LittellbJay R.AldercNathanTeatsa

aNorthern Region, U.S. Forest Service, Missoula, MT, USA

bU.S. Department of Interior Alaska Climate Adaptation Science Center, U.S. Geological Survey, Anchorage, AK, USA

cU.S. Geological Survey, 104 CEOAS Admin Building, Oregon State University, Corvallis, OR 97331, USA

Received 28 July 2021, Revised 16 December 2021, Accepted 17 December 2021, Available online 22 December 2021.

https://doi.org/10.1016/j.crm.2021.100385

Under a Creative Commons license

Abstract

Anthropogenic climate change during the 21st century presents a significant challenge to the protection of cultural resources (CRs) on federal lands that encompass ∼ 28% of the U.S. In particular, CRs on this land base may be adversely affected by a wide range of climate-change hazards, including damage by sea-level rise, enhanced deterioration by increasing temperature and precipitation, and destruction by more-frequent and severe wildfire. Most current measures to manage the impacts of hazards on CRs use vulnerability assessments, but because these require that all CRs be treated as having an equal chance of being affected by climate-change hazards (i.e., equal exposure) across large landscapes, the cost and resources required for such analyses are overwhelming to land management agencies. Projections of changes in many hazards, however, show that the probability of hazard occurrence will be unevenly distributed on the landscape. Incorporating this information into a risk assessment thus allows CR managers to prioritize their efforts on assessing impacts to CRs in those areas where the probability of the hazard is greatest, thus increasing efficiency. We provide several heuristic examples of implementing the first part of a CR risk assessment by using 21st-century projections of several hazards most likely to adversely affect CRs on nine National Forests (NFs) managed by the U.S. Forest Service in northern Idaho and Montana. Overlaying the projected distribution of hazards on these NFs with the distribution of CRs identifies CR exposure that, with information on their vulnerability, is required to determine risk. Additional policy and field studies will be needed to determine how to prioritize those CRs that are most at risk according to their significance as well as identify how impacts can be reduced and managed through adaptation planning and implementation. Adaptation will follow the iterative risk management process particularly by improving projection resolution. Finer scale, process-based modeling informed by the highest priority CRs would also provide a means to assess various adaptation options that might change the estimated risk and increase the odds of CRs being as little affected as possible.

1. Introduction

By encompassing a broad range of categories and types, including diverse archaeological sites left by Indigenous peoples, structures built by Euroamerican explorers, pioneers, and prominent historical figures, and cultural landscapes that played a role in the mythology, religion, or history of a people, cultural resources (CRs) in the United States provide critical insights into the history and development of human activities and cultural traditions spanning thousands of years (Neumann et al., 2010). They are nonrenewable and, in many cases, their significance is based on their existing location. In this regard, they are particularly vulnerable to the impacts of climate change because they cannot adapt, they are irreplaceable, and significant site context and integrity is lost if they are altered or moved.

Federal and many state land-management agencies in the U.S. are tasked with protecting CRs from adverse effects that occur on their land base. Present and future anthropogenic climate change is widely recognized as posing a significant adverse effect to CRs (Colette, 2007Fatoric and Seekamp, 2017aSabbioni et al., 2010Sabbioni et al., 2009Sesana et al., 2021). Responding to this threat from climate-change hazards such as wildfire, temperature extremes, and sea-level rise is particularly challenging for the tens of thousands of CRs on U.S. public lands where, under federal historic preservation laws, regulations, and policies that call for their protection and stewardship, new impacts from climate change will demand additional resources that will likely exceed current capacity of land-management agencies (Fatoric and Seekamp, 2017cSesana et al., 2018Sesana et al., 2020Xiao et al., 2021). It is thus imperative that management plans addressing impacts of climate change to CRs on public lands develop the most efficient strategies possible to assist with decision making, recognizing that the scale of the problem means that the loss of some CRs will be unavoidable (Heilen et al., 2018).

Here we outline a new strategy for developing a risk assessment for CRs and implementing it for CR management and adaptation. We then provide several heuristic examples of implementing this strategy that involve identifying the probability of several projected hazards occurring on nine National Forests (NFs) managed by the U.S. Forest Service that cover ∼ 24 million acres in the Rocky Mountains of northern Idaho and western Montana (Fig. 1). Our results demonstrate that this strategy will provide a more effective means for decision makers to manage CRs for climate-change impacts through adaptation.

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Fig. 1. Map showing U.S. National Forests (green shaded areas) and Bailey’s ecosections (outlined by black lines) in northern Idaho and Montana, with state boundaries shown in blue. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2. Strategies for managing cultural resources for Climate-Change impacts

2.1. Existing strategies

Nearly all adverse effects to CRs that are currently considered are associated with activities such as resource extraction, construction, deferred maintenance, wildfire suppression, and post-fire rehabilitation. These activities often implement protocols to mitigate adverse effects that were either designed during pre-planning preparations or are based on experience working with CRs in contexts other than climate-change hazards. The specific actions used to mitigate adverse effects may change and adapt to incident conditions, but in all cases, protocols are based on past or present experience and can be considered as reactive adaptative strategies (Sesana et al., 2018). Some agencies may also apply forest-management practices such as fuels reduction to reduce the likelihood of wildfire in the coming years. We note, however, that while these activities have the added value of reducing impacts to CRs in the associated project areas, they are not uniformly applied across federal lands, they are not yet fully designed to address the specific impacts from anthropogenic climate change, and they do not assess risk.

Proactive adaptive strategies are designed to manage and adapt to potential adverse effects from future climate-change hazards (Sesana et al., 2018). Having this longer-term perspective provides CR managers with time to marshal their resources and strategically implement adaptation measures for exposed and vulnerable CRs, with the benefits of such proactive measures exceeding their costs (Lempert et al., 2018) as well as enabling land management agencies to meet their long-term CR stewardship responsibilities under federal law.

The current proactive strategy adopted by most federal land-management programs is centered on a vulnerability assessment (Halofsky et al., 2018aHalofsky et al., 2018bMcNeeley et al., 2017Rockman et al., 2016) (https://www.fs.usda.gov/managing-land/sc/vulnerability-assessments), which was a focus of the Third and Fourth Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) that addressed impacts, adaptation, and vulnerability (McCarthy et al., 2001Schneider et al., 2007). Here the expected impact of climate change to a CR (e.g., its vulnerability) is determined by the nature and degree to which a CR is exposed to significant climatic variations (e.g., exposure) plus the degree to which a CR could be affected by climate change (e.g., sensitivity) minus the capacity of the CR to adapt (Fussel and Klein, 2006). Rockman et al. (2016) suggested that because most CRs are inanimate and thus have little capacity to adapt, management plans should consider adaptation separately, leaving a vulnerability assessment as just a function of exposure and sensitivity. Relatively few climate-change-related vulnerability assessments have been put into practice, however, in part because all CRs are often treated as having an equal chance of being affected by climate-change hazards (i.e., equal exposure) across large landscapes. Because every CR then needs to be evaluated for its sensitivity, the cost and resources required for such analyses are overwhelming to land management agencies.

2.2. Proposed strategy

We propose that framing these proactive measures in the context of a risk assessment will provide a more effective means for decision makers to manage CRs for climate-change impacts through adaptation. We adopt the risk assessment framework that became the focus of the most-recent IPCC reports addressing impacts, adaptation and vulnerability (Field et al., 2014Field et al., 2012Oppenheimer et al., 2014) as well as of the Fourth National [U.S.] Climate Assessment (Lempert et al., 2018Reidmiller et al., 2018). The level of risk of climate-related impacts to CRs results from the interaction of the probability of occurrence of climate-change hazards with the exposure and vulnerability of the CR and “involves judgments about what is of value, ranking of priorities, and cost–benefit analyses” (Jay et al., 2018). Here exposure is considered separately from, rather than being part of, vulnerability, with the former now considered as the presence of CRs in places that could be adversely affected by a hazard and the latter being the characteristics of CRs that are sensitive to climate-change hazards (Oppenheimer et al., 2014).

A risk assessment then builds on a vulnerability assessment by considering how vulnerability and exposure interact with the probability of a hazard occurring to determine the level of risk that a CR has to climate change. For example, a wooden historic structure that occurs in a dense forest is vulnerable to wildfire, but if there is a low probability of wildfire occurring, then it has low exposure and is thus at lower risk than a similarly situated wooden structure occurring in an area with a high probability of wildfire. This contrasts with a vulnerability assessment which, by not accounting for the probability of wildfire occurring, would require that both structures be managed equally for the impacts from wildfire. The fact that climate-change hazards will be spatially heterogeneous as opposed to uniformly distributed thus makes a risk assessment particularly useful for prioritizing CRs for adaptation and management that would otherwise not be possible with a vulnerability assessment.

We outline a seven-step framework for developing a risk assessment for CRs and implementing it for CR management and adaptation, where the first four steps are required to define risk and the last three steps apply the assessment to address management and adaptation. The first step uses model projections to evaluate the probability of one or more climate-related hazards occurring on the landscape. Step 2 then involves assessing the level of exposure of CRs to the hazard, whereby CRs will have the greatest exposure when located in areas of greatest probability of a hazard. Once these CRs are identified, one assesses their level of vulnerability (step 3) from which the level of risk can then be established (step 4). Prioritizing CRs for adaptation, however, may require considering their cultural, scientific, or architectural significance in addition to their level of risk (step 5), which then guides decision making in the implementation of adaptation measures that reduce risk by reducing exposure and vulnerability (step 6). Adaptation measures are then reevaluated through iterative risk management that involves an ongoing process of assessment, action, monitoring, and reassessment as new information becomes available that will continue for decades or longer (step 7).

Protocols for evaluating vulnerability and significance and for implementing adaptation measures (with attendant budgetary implications) are reasonably well established (Fatoric and Seekamp, 2017bFatoric and Seekamp, 2018Rockman et al., 2016Sesana et al., 2021Xiao et al., 2021). Heilen et al. (2018) proposed using model predictions of climate-change hazards to identify CRs at risk. To our knowledge, however, only Sabbioni et al., 2010Sardella et al., 2020 have provided projections specifically designed to quantify 21st-century hazards to CRs. The former study emphasized gradual climate changes in central Europe and their impact on components of the built environment (wood, stone, metal). The latter study emphasized extreme temperature and precipitation events across central Europe with the goal being to use these projections for future risk assessments (Cacciotti et al., 2021).

Here we extend these approaches by providing heuristic examples of our strategy that identify the probability of several projected hazards occurring on nine US National Forests in the Rocky Mountains of northern Idaho and western Montana (Fig. 1). The hazards we consider are wildfire and precipitation and temperature extremes. Overlaying maps of the projected hazards on corresponding NFs allows evaluation across multiple hazards and identifies those areas on the NFs that are simulated as having the highest probability of an increase in those hazards under climate change, which we refer to as “hazard hotspots” (Cacciotti et al., 2021). CRs at highest risk can then be determined by identifying those that are present in hazard hotspots (i.e., having highest exposure) with highest vulnerability. A risk assessment allows resource managers to focus their efforts on fewer CRs than would be the case with a vulnerability assessment, thus making management plans for adaptation with existing capacity and resources more efficient.

3. Climate-change hazards

Climate-change hazards that cause severe damage or loss pose an increasing threat to CRs, where weather and climate extremes represent a major component of these hazards. One common thread underlying all hazards associated with anthropogenic climate change (e.g., sea level, temperature, precipitation, drought, snowpack, wildfire, floods) is that they will be unevenly distributed on the landscape and may change with time (Buotte et al., 2019Littell et al., 2018Sweet et al., 2017Wehner et al., 2017). In contrast, the current state-of-the art in assessing impacts to CRs from other future hazards due to anthropogenic climate change, including wildfire, is to acknowledge that the hazard will likely increase (Davis, 2018bFlanigan et al., 2018Holtz et al., 2014Rockman et al., 2016) and then identify specific details of how the hazard can damage CRs (Flanigan et al., 2018Rockman et al., 2016Ryan, 2010Ryan et al., 2012Sesana et al., 2021). None of these efforts, however, consider the spatial heterogeneity of the hazards.

Projections of changes in temperature and precipitation that underlie many hazards (e.g., temperature extremes, drought, wildfire, floods) have been available largely through the climate modeling efforts for the Intergovernmental Panel on Climate Change (IPCC) (Stocker et al., 2013). Significant progress has been made in downscaling and delivering these climate-model projections at the high spatial resolution needed by resource managers to quantify hazard distribution for a risk assessment (Lipschultz et al., 2020) (https://www.fs.usda.gov/managing-land/sc/data-dashboard), and such deliverables will continue to improve with increasing model resolution (Gutowski et al., 2020Sardella et al., 2020), improvements in downscaling (Lipschultz et al., 2020Pierce et al., 2014), and improvements in process models.

Assessments of historical changes in and projections of wildfire have largely only developed in the last two decades (Wehner et al., 2017), but their findings clearly indicate that quantifying the impacts of future wildfire to CRs is also now warranted. For example, there has been a significant increase in wildfire activity in the western United States over the last three decades, as reflected by area burned (Littell et al., 2009Parks and Abatzoglou, 2020) and the number of large fires (Dennison et al., 2014). Attribution studies suggest that anthropogenic climate change has caused the cumulative area burned in the western U.S. to nearly double since 1984 (Abatzoglou and Williams, 2016) and to increase by a factor of 7–11 in adjacent western Canada (Kirchmeier-Young et al., 2019). Finally, projections suggest that wildfire area and possibly severity will likely continue to increase through the remainder of this century associated with increasing anthropogenic emissions of greenhouse gases, albeit with strong spatial heterogeneity (Abatzoglou et al., 2019Buotte et al., 2019Halofsky et al., 2020Littell et al., 2018). Projections of increased wildfire activity and the associated impact on CRs thus also makes it a key consideration for CR management plans addressing climate change.

4. Projections of climate-change hazards: Methods

Climate projections that underlie hazard projections are based on (i) scenarios that describe plausible societal actions from now into the future, (ii) the likely greenhouse gas (GHG) emissions trajectories associated with those actions and corresponding GHG concentrations in the atmosphere, and (iii) the resulting climate changes as simulated by global climate models. Because scenarios are developed in a risk-based framework with a high degree of uncertainty about future societal developments, the primary scenarios used in policymaking contexts are usually not assigned a formal likelihood of occurrence, but instead provide a range of plausible outcomes (i.e., projections as opposed to predictions) (Terando et al., 2020).

Hazard projections used here are associated with two scenarios developed for the IPCC. The SRES A1B scenario (from IPCC AR4) describes a balance between fossil-fuel intensive technologies and non-fossil-fuel energy sources (Nakićenović and Swart, 2000). The RCP8.5 scenario (from IPCC AR5) is based on high energy demand and GHG emissions in the absence of climate change policies (Collins et al., 2013). Climate projections used for one set of wildfire projections used here (Littell et al., 2018) were run prior to AR5 and so used the SRES A1B scenario, whereas all other projections used in this study were run following AR5 when the RCP8.5 scenario was available. Projected mid-21st century global warming is similar for the two scenarios (1.5–2.0 °C) with increasing differences occurring by the end of the century (globally ∼ 3 °C for A1B and ∼ 4.5 °C for RCP8.5) (Collins et al., 2013).

We use projections of several hazards that are expected to increasingly cause damage to CRs in the interior mountainous regions of the western U.S. (Davis, 2018aDavis, 2018bFlanigan et al., 2018Rockman et al., 2016). Projections of temperature and precipitation extremes are derived from climate model simulations that we statistically downscaled and bias corrected by Localized Constructed Analogs (LOCA) (Pierce et al., 2014)), while two independent projections of wildfire are from Littell et al., 2018Buotte et al., 2019).

4.1. Temperature and precipitation extremes

The climate projections from LOCA used here are based on an ensemble of 32 global climate models (GCMs) forced by the RCP8.5 scenario and are downscaled to 1/16° × 1/16° (∼6‐km × 6‐km) (Lipschultz et al., 2020)). We then used these data to calculate the change in the median of climate extremes for the 2020–2049 (“2030s) and 2070–2099 (“2080s”) time intervals relative to the historical (1980–2009) in order to capture near-term changes and those occurring later in the century, respectively. Following Sardella et al. (2020), we calculate three climate extreme indices defined by the Climdex Project (www.climdex.org) that are directly relevant to impacts to CRs. The temperature Tx90p index is the percentage of days in a year when daily maximum temperature is greater than the 90th percentile. Such extreme temperatures adversely affect CRs through increased decay of organic and stone archaeological structures and materials (Rockman et al., 2016). Precipitation increases, especially in the extremes, can potentially cause damage to CRs through increased site erosion from surface runoff or mass movement, or through increased wood rot or masonry decay, whereas precipitation decreases, especially leading to drought, can cause damage through increased exposure of sites to erosion or also through decay of wood or masonry (Rockman et al., 2016). We used two indices of precipitation extremes. The CDD index (Consecutive Dry Days) is the maximum number of consecutive days in a year with precipitation being < 1 mm/day, and is thus an indicator of lengthening of the dry season. The R20mm index is the number of days in a year with precipitation ≥ 20 mm/day, thus reflecting an increase in heavy rain that may lead to increased erosion through surface runoff or landslides.

4.2. Wildfire

We use published projections of change in wildfire for the western U.S. for 2030–2059 (“2040s”) (Littell et al., 2018) and 2020–2049 (“2030s”) (Buotte et al., 2019) relative to the historical period (1980–2009). Although the two projections are based on different climate models and scenarios and are presented at different spatial scales, the differences between the two scenarios (A1B and RCP8.5) are negligible for the time intervals of the two projections (2030s and 2040s). Any agreement between the two independent lines of evidence thus helps establish confidence in their results.

Littell et al. (2018) (herein L18) developed a new set of ecologically based fire projections across the western U.S. at the scale of Bailey’s ecosections, which are defined primarily by vegetation within a related climate type (Bailey, 1995). The rationale for using the ecosection scale was to represent climate and wildfire at scales closer to those relevant for resource management while retaining sufficient statistical representation of fires in the historical record (Littell and Gwozdz, 2011). L18 classified climate-fire relationships for Bailey’s ecosections that represent a range of fire climatologies from fuel limited to flammability limited. Purely fuel-limited ecosections are typically drier ecosystems where fuels are dry enough to burn most years, but abnormally wet years in the year prior to fire (e.g., greater potential evapotranspiration, greater precipitation, greater snow water equivalent, etc.) can create larger or more connected fuels that then lead to larger fires.

Purely flammability limited ecosections are associated with wetter forests where it takes an abnormally warm, dry year or years (e.g., less precipitation, less snow water equivalent, etc.) to make normally wet fuels more flammable. L18 developed an F index that indicates the position of each ecosection in the historical period along the continuum from fuel to flammability limitation. This produces a continuous variable that describes the climatic controls of fire area burned from completely fuel limited (-1) to completely flammability limited (1). Ecosections in northern Idaho and western Montana have more positive F indices (>0) indicating that they are dominantly flammability limited.

The L18 wildfire projections are based on climatologies for the 2040s derived from the mean of ten global climate models forced by the A1B scenario. L18 used statistically downscaled climate model results at 1/16° × 1/16° (∼6‐km × 6‐km) for the western United States derived from Littell et al. (2011). The statistically downscaled temperature and precipitation were then used to drive a hydrologic model to derive important ecoclimatic variables such as potential evapotranspiration and snow water equivalent (Elsner et al., 2010). The future ecoclimatic variables were then used as predictors in the ecosection statistical climate-fire models to project expected area burned contingent on future climate at the scale of the ecosections.

We used historical data compiled by L18 to determine the mean of acres burned for 1980–2009 (historical) for each of the ten ecosections that occur in northern Idaho and western Montana (Fig. 1Fig. 2). We then derived the mean of simulated area burned for each ecosection from 91 realizations of the climate projected for the 30-yr periods for the 2040s. These are the projections for the mean climate from the ten different global climate models under the A1B emissions scenario. The historical data were then used as the baseline for determining the projected relative changes in area burned during the 2040s for each ecosection. The relative changes, which we refer to as increase factors, provide a means of quantifying the probability of wildfire that contributes to the risk of wildfire to CRs. Fig. 2 shows anywhere from a two-fold to 26-fold increase in the projected mean area burned in each of the ten ecosections relative to the historical area burned.

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Fig. 2. Box plots of historical (1980–2009) (yellow) and 2040s projected (red) area burned by ecosection in the study area (units are in log10 ha). Increase factors (shown below 2040s box plots) are ratios of 2040s/historical acres burned. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

We next used a Geographic Information System (GIS) to determine the area of each ecosection that occurs on the nine NFs in northern Idaho and Montana (Table 1). We used USFS data to determine the historical acres burned on each NF and, assuming the area burned was equally distributed across the Forest, allocated this into each of the ecosection areas on the Forest (Table 1). We then multiplied the historical acres burned for each ecosection area on the Forest by the increase factors derived above from each of the ecosections to determine the mean estimated increase in area burned and associated increase factors on the NFs during the 2040s (Table 1).

Table 1. Summary of acres burned in ecosections covering U.S. National Forests.

Ecosection% of ForestHistorical acres burned2040s acres burned2040s increase factor
Beaverhead-Deerlodge National ForestM331A1%19222052510.7
M332A1%19224174221.7
M332B19%365141022402.8
M332E79%1518235222703.4
Bitterroot National ForestM332A63%442692300000021.7
M332B37%2599947279822.8
Custer Gallatin National Forest331G16%2199066773113.1
M331A62%852136300000010.7
M331B2%2748824740.1
M332D10%1374412858782.1
M332E5%687212363993.4
Flathead National ForestM332B9%539741511272.8
M333B34%203902300000026.3
M333C55%32984124144347.3
M333D2%1199415268612.7
Helena-Lewis and Clark National ForestM332B20%1353593790052.8
M332C9%60911153314225.2
M332D59%3993098305622.1
M332E3%20304698453.4
M333C8%541443963317.3
Idaho Panhandle National ForestsM333A28%7925262333.3
M333B8%22645950926.3
M333D64%1811523060812.7
Kootenai National ForestM333B81%113515298316626.3
M333D19%2662733896012.7
Lolo National ForestM332A3%1114924215421.7
M332B28%1040562913572.8
M333B6%2229858598526.3
M333C1%3716272037.3
M333D63%234127298043112.7
Nez Perce-Clearwater National ForestM332A59%449914300000021.7
M333D27%205893262101512.7

Wildfire projections by Buotte et al. (2019) (herein B19) are based on simulations for the 2030′s using two GCMs forced by the RCP8.5 scenario. GCM climate data were then downscaled to run the Community Land Model (CLM) at 1/24° × 1/24° (∼4‐km × 4‐km) resolution over the western U.S., or considerably higher than the scale of ecosections. The CLM calculates multiple biophysical and biogeochemical processes, including surface heat fluxes, photosynthesis, evaporation, transpiration, and decomposition. B19 enhanced the ecological resolution of CLM by refining the definitions of CLM’s default plant functional types (PFTs) into thirteen PFTs.

The CLM fire module is a process-based global fire parameterization scheme that simulates fire occurrence and fire spread (Li et al., 2012). For fire occurrence, the number of fires is determined by ignition counts largely due to fuel load, fuel moisture, and human suppression. Buotte et al. (2019) modified the upper and lower fuel limits within grid cells to correspond to the range of forest biomass in their domain. Average burned area of a fire is then determined by average fire spread rate and fire duration. B19 found that over the western United States, their modified CLM estimates of area burned were highly correlated with observations (R2 = 0.75).

The B19 wildfire projections are based on the difference in simulated future (2020–2049) minus simulated historical (1980–2009) area burned weighted by the future area burned. The resulting distribution of weighted differences in area burned was classified into low (<10%), medium (>10%, <100%), and high (>100%). Final wildfire fire rankings that accounted for differences in climate projections were assigned uncertainty classes, with Uncertain (=1) being one GCM low and one high, Low (=2) where both GCMs are low, Med‐Low (=3) with one low and one medium, Medium (=4) with both GCMs medium, Med‐High (=5) with one medium and one high, and High (=6) where both GCMs are high.

5. Projections of climate-change hazards for US National Forests in the northern Rocky Mountains

5.1. Temperature and precipitation extremes

Fig. 3 shows the projected median climate extremes for the nine NFs for the 2030s and 2080s. In both cases, there are strong spatial variations in the extremes, while at any given location, the increases seen in the 2040s relative to the historical continue into the 2080s reflecting the continuation of the local trend throughout the century (Fig. 4). Areas of NFs in northern ID and northwestern MT are projected to experience the largest increases in CDD (Fig. 3A, D), whereas areas of NFs in the northern ID and southwestern MT parts of our domain will experience the largest increases in temperature extremes (Fig. 3B, E). The largest increases in precipitation extremes, on the other hand, occur primarily in association with high-elevation mountainous areas (Fig. 3C, F) reflecting an increase in orographic precipitation that characterizes the region today, with some of these increases occurring in areas of large increases in CDD or temperature extremes.

Fig. 3 shows the projected median climate extremes for the nine NFs for the 2030s and 2080s. In both cases, there are strong spatial variations in the extremes, while at any given location, the increases seen in the 2040s relative to the historical continue into the 2080s reflecting the continuation of the local trend throughout the century (Fig. 4). Areas of NFs in northern ID and northwestern MT are projected to experience the largest increases in CDD (Fig. 3A, D), whereas areas of NFs in the northern ID and southwestern MT parts of our domain will experience the largest increases in temperature extremes (Fig. 3B, E). The largest increases in precipitation extremes, on the other hand, occur primarily in association with high-elevation mountainous areas (Fig. 3C, F) reflecting an increase in orographic precipitation that characterizes the region today, with some of these increases occurring in areas of large increases in CDD or temperature extremes.

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Fig. 3. Projected median climate extremes for the nine National Forests for the 2030s and 2080s relative to the historical (1980–2009). (A) CDD (Consecutive Dry Days) index for 2030s. (B) Tx90p index (percentage of days in a year when daily maximum temperature is greater than the 90th percentile) for 2030s. (C) R20mm index (number of days in a year with precipitation ≥ 20 mm/day) for 2030s. (D) CDD index for 2080s. (E) Tx90p index for 2080s. (F) R20mm index for 2080s. We only show median values where > 60% of the 32 models in the ensemble agree on the sign of change.
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Fig. 4. (ATime series of the CDD (Consecutive Dry Days) index for 1950–2100 relative to the historical (1980–2009) for six different grid locations on National Forests within the Northern Region. (B) Time series of the Tx90p index (percentage of days in a year when daily maximum temperature is greater than the 90th percentile) for six different grid locations on National Forests within the Northern Region. (C) Time series of the R20mm index (number of days in a year with precipitation ≥ 20 mm/day) for six different grid locations on National Forests within the Northern Region. Custer Gallatin National Forest: 45.2218505557156, −109.92090831954312. Helena-Lewis and Clark National Forest: 46.90238553920221, −110.77268944816251. Northern Nez Perce-Clearwater National Forest: 46.817210415033195, −115.06179047298947. Southern Nez Perce-Clearwater National Forest: 45.759327993592244, −115.33549090662291. Cabinet Mountains Wilderness: 48.20894763512027, −115.69873239031664. Idaho Panhandle National Forest: 47.87846233086076, −116.3145633989287.

5.2. Wildfire

Increase factors for the 2040s derived from the L18 projections (Fig. 2) and the B19 wildfire projections for the 2030s identify the spatial distribution of wildfire probability on each NF in the next few decades relative to the historical (Fig. 5). Despite the differences in resolution, both projections agree in showing large increases in wildfire on the Custer-Gallatin NF, the southern part of the Nez Perce-Clearwater NF, the northern Kootenai NF, and the northern Helena-Lewis and Clark NF, increasing confidence in the projections for these areas. Both projections also show that the probability of wildfire increase will be spatially variable. Specifically, increase factors from the L18 projections for five of the ecosections are 2, 3, 3, 3, and 7, whereas increase factors for the other five ecosections are considerably higher at 11, 13, 22, 25, and 26. Similarly, the B19 projections are largely either Low or High.

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Fig. 5. (A) Projected increase factors on National Forests in northern Idaho and Montana for the 2040s relative to historical based on Littell et al. (2018) (L18) projections. (B) Probability of wildfire occurrence for the 2030s relative to historical on National Forests in northern Idaho and Montana from Buotte et al. (2019) (B19), referenced from Uncertain (=1) to High (=6).

At the same time, however, there are clear differences between the two projections. First, the higher resolution CLM model used by Buotte et al. (2019) finds greater spatial variability across each NF than identified at the ecosection scale. These differences are expected as they largely reflect the assumption that the 2040s increase factors determined for each ecosection are stationary linear responses to climate and can be applied across the ecosection equally. Second, the L18 projections for the NFs in west-central Montana (Bitterroot, Beaverhead-Deer Lodge, Lolo, and southern Helena-Lewis and Clark) suggest much lower probabilities of wildfire than the B19 projections (Fig. 5). Such inter-model disagreement decreases confidence in the projections for these areas, which would be factored into the assessment of hazard probability. We note, however, that compared to climate modeling, modeling of projected wildfire change is still in its early stages, with L18 and B19 being the most recent examples of only a few such modeling efforts conducted thus far for the western U.S. Further work is needed to understand the differences identified in Fig. 5 and new projections would be included during the iterative risk management process.

6. Discussion

6.1. Implications for management

We developed and applied a framework for assessing climate vulnerability to CRs, and CR managers can follow the approach outlined here to prioritize known sites for evaluation according to their association with hazard hotspots (i.e., exposure). Given that risk is represented as the probability of occurrence of hazards multiplied by the impacts if those hazards occur (Oppenheimer et al., 2014), CRs at highest risk will only occur in hazard hotspots. Prioritizing hazard hotspots for establishing CR exposure thus increases efficiency relative to measures that are based on still widely used vulnerability assessments which do not evaluate hazard probability (Halofsky et al., 2018aHalofsky et al., 2018bMcNeeley et al., 2017Rockman et al., 2016). As an example of this, we use the L18 projections of increase area burned to show that, even at the scale of ecosections, accounting for the spatial variability in burned area across the landscape translates to efficiency in assessing CR risk to wildfire. Specifically, the cumulative area represented by the highest increase factors on each NF in our study region is 35% of the total area (Fig. 5A). Given that a risk assessment will prioritize CRs in these hazard hotspots, this represents a 65% reduction in area where efforts to evaluate CR exposure and vulnerability need to be emphasized. The B19 projections demonstrate how increasing the spatial resolution of the projections can further increase efficiency in assessing CR risk to wildfire. Specifically, the cumulative area represented by the High projections on the NFs is 17% of the total area (Fig. 5B), representing a 83% reduction in area where initial efforts to manage for climate change impacts to CRs need to be concentrated.

Given that climate change may result in an increase in multiple hazards in any given region, with each having their own spatial pattern of hazard hotspots (Fig. 3Fig. 6), CR managers may face scenarios where differing areas of their land base will have differing hazard hotspots, thus requiring climate risk management plans specific to each land base to address any differences in impacts posed by the hazards to the CRs. At the same time, some areas may have overlapping hazard hotspots which would compound the hazard impact as well as present challenges if adaptation measures to reduce risk from each hazard conflict. Knowing which hazards are likely to occur, however, means that CR managers will only need to assess risk from them rather than address the full range of possible hazards that would be required in a vulnerability assessment.

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Fig. 6. Distribution of hazards projected for the 2030s for the Custer-Gallatin and Kootenai National Forests. We only show median values for climate indices where > 60% of the 32 models in the ensemble agree on the sign of change. (A) CDD index for the Custer-Gallatin National Forest. (B) Tx90p index for the Custer-Gallatin National Forest. (C) R20mm index for the Custer-Gallatin National Forest. (D) Wildfire projections for the Custer-Gallatin National Forest for the 2030s (Buotte et al., 2019). (E) CDD index for the Kootenai National Forest. (F) Tx90p index for the Kootenai National Forest. (G) R20mm index for the Kootenai National Forest. (H) Wildfire projections for the Kootenai National Forest for the 2030s (Buotte et al., 2019).

Fig. 6 illustrates these points by comparing the distribution of the four hazards projected for the 2030s that are considered here for the Custer-Gallatin and Kootenai NFs. CR managers on the Custer-Gallatin NF would primarily address impacts from widespread wildfire with areas of lengthening of the dry season (CDD) in the western part of the NF, whereas CR managers on the Kootenai NF would primarily address impacts from widespread CDD with areas of extreme temperatures in the northwestern quadrant of the NF and areas of potential erosion in the southwestern quadrant.

The next step in our framework involves establishing the level of exposure of a CR to the hazard. CRs with highest exposure will occur in hazard hotspots, thus allowing CR managers to identify which CRs to prioritize for evaluating vulnerability, resulting in a further increase in efficiency. For example, consider two NFs where the distribution of hazard hotspots and the number of CRs is the same. On the first NF, however, 90% of the CRs are in hazard hotspots, whereas on the second NF, only 10% of the CRs are in hazard hotspots, resulting in 88% fewer highly exposed CRs that would be prioritized for evaluation of vulnerability than on the first NF. This has important implications for managing risk on different administrative land units. Because U.S. federal laws require that the location of CRs on U.S. federal lands not be publicly disclosed, Fig. 7 illustrates this point by comparing the B19 wildfire projections with the distribution of hypothetical CRs on the Kootenai and Custer-Gallatin NFs whose locations were randomly generated.

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Fig. 7. (A) Wildfire projections on the Kootenai National Forest for the 2030s (Buotte et al., 2019). (B) Location of randomly located hypothetical CR sites (small black symbols) on the Kootenai National Forest. (C) Wildfire projections for the Custer-Gallatin National Forest for the 2030s (Buotte et al., 2019). (D) Location of randomly located hypothetical CR sites (small black symbols) on the Custer-Gallatin National Forest.

In our hypothetical example, both NFs have similar numbers of CRs (232 on the Kootenai NF, 186 on the Custer-Gallatin NF), whereas wildfire projections for these two NFs show a significantly larger area of high-probability wildfire occurring on the Custer-Gallatin NF than on the Kootenai NF. In these examples, only 2% (n = 4) of the CRs on the Kootenai NF are highly exposed, while 32% (n = 60) of the CRs on the Custer-Gallatin NF are highly exposed, thus identifying different capacity and budget needs for each NF’s climate risk management plan.

This strategy can also be applied to CRs that have yet to be identified by using predictive models (Heilen et al., 2018) and inventory plans that NF CR managers commonly develop to assist in their cultural resource survey work. These models are based on the relationship between known CR sites and characteristics of the landscape in which they occur. The most significant predictor of CR location is typically slope angle, with a clear majority of CRs commonly occurring on 10-15° slopes. In developing CR climate risk management plans where no inventory has yet been completed, CR managers can thus query NF-specific predictive models and if, for example, the strongest predictor is slope, use a GIS to overlay digital elevation models on hazard maps to predict areas with the highest concentration of CRs with highest exposure.

6.2. Projection uncertainties

Adaptation and risk management policies and practices should recognize and explicitly include uncertainties that underlie a risk assessment (Lavell et al., 2012Terando et al., 2020). Climate-change projections that are the basis for calculating hazards are associated with at least three sources of uncertainty. The first is related to how closely the greenhouse gas emission scenarios that underlie the projections (A1B and RCP8.5) will track actual emissions that occur this century. Schwalm et al. (2020) noted that the RCP8.5 emission scenario, which is the basis for most of our projections, is consistent with historical cumulative carbon emissions, remains consistent with emissions projected for the mid-21st century under current policies, and may still be plausible by the end of this century. Moreover, differences between the A1B and RCP8.5 scenarios used for the mid-21st century projections in this region are comparatively small for all but the latest 21st century. In any event, this source of uncertainty can be readily addressed as part of the iterative risk management process by monitoring emissions and, if necessary, implement new projections based on better constrained emission scenarios at a later time.

The second major source of uncertainty in climate-change projections for a given future scenario is associated with differences among climate models that reflect the complexity and large number of processes they include and the different ways in which these processes are represented (Collins et al., 2013). For the climate-extreme hazards reported here, we addressed this uncertainty by only showing median values where > 60% of the 32 models in the ensemble agree on the sign of change (Fig. 3). We also assessed whether the spatial patterns of the median hazard distributions are stationary within the minimum to maximum ranges of these results. For all three indices, these patterns are replicated, although their geographic extent and intensities decrease for the minimum ranges and increase for the maximum ranges (Fig. 4). This replication gives confidence in the spatial pattern, while the maximum values could be used to address risk tolerance.

A third major source of uncertainty is interannual-to-decadal climatic variability that affects the northern Rocky Mountain region but is often more temporally persistent than many GCM simulations and can be muted by ensemble mean approaches to future climate projections. Using multi-decadal climatologies and a range of scenarios generally limits the potential cascading uncertainty associated with this variability (Terando et al., 2020), but the risk analysis could recognize the potential for observations to either exceed or subceed model (especially ensemble) projections for a year or years at a stretch for a given region.

Another source of uncertainty in using model projections are the downscaling methods (modified delta similar to BCSD for L18, LOCA for extremes). The climate metrics extremes calculated here are dependent on the downscaling and bias correction methods that modify the simulated variable distributions, which is especially relevant for the thresholds used in R20mm and CDD.

Of the hazards assessed here, wildfires will likely have the largest impact on CRs given that they will be widespread, often severe, and pose an immediate threat when they occur as opposed to the more gradual timelines associated with damages from temperature or precipitation changes. Evaluating uncertainties in wildfire projections is thus particularly important in assessing their impacts. Uncertainties include those associated with the underlying climate projections described above as well as those associated with the models used to derive wildfire occurrence from model climatologies.

Uncertainties in the L18 projections include the assumption that the 2040s increase factors determined for each ecosection are stationary linear responses to climate and can be applied across the ecosection equally. Not all vegetation or fuels within a given ecosection were equally available historically, however, nor do we expect them to change at the same rate in the future. Furthermore, it is likely that those fuels that contributed most to the historical fires will be most and first available during the early part of the 21st century under climates most resembling the historical, whereas during the latter part of the century, vegetation and fuel types and their availability might be quite different.

The last two fire years in California illustrate two things worth noting in this regard. First, some of the projected changes in mean annual area burned appear very large, but regionally, extreme years much larger than the historical mean can occur and are surprising with only the historical regime as a reference. Whether such events become as common as implied by the projection means depends on climate, fuels, and ignition (including changes in human ignitions). Second, under extreme conditions (relative to historical), fire areas indicated by the future analysis could occur earlier than the time frame projected.

Applying statistical fire area projections in these landscapes is also limited by modeling assumptions. Among them are that: (1) we assume regional climate was and is the main driver of fire area burned – other drivers, such as vegetation and management, were not explicitly modeled; (2) the changes in future area burned will be linear extensions of observed changes – some fire regimes may approximate that assumption, but others may not; and (3) the ecosection-scale changes in fire responses will be equally distributed across the ecosections and that the differences among them transition at ecosection boundaries. As a result, in the example shown here, there is some chance that our climate-fire modeling assumptions may result in some places with fewer impacts than predicted and other places with more impacts than predicted.

The B19 wildfire projections also include several assumptions that introduce uncertainty, leading Buotte et al. (2019) to conclude that areas identified as having highest probability of an increase in area burned does not necessarily mean that they will burn or burn with high intensity if ignited. First, simulated fire ignition is estimated from observations of historical lightning strikes, with no reference as to how the frequency of lightning strikes may change in the future. Similarly, forest distributions are based on existing PFTs and do not change through the period of the projection. Finally, as with the L18 projections, the CLM fire module does not account for human-caused fire, fire suppression policies, or suburban and rural population sprawl, all of which may have a large effect on area burned. We also note that neither of the two wildfire projections used here estimate fire severity, which may be particularly important in evaluating CR vulnerability.

One solution to these shortcomings in existing wildfire projections is to begin doing finer scale, process-based fire modeling, informed by the highest priority CRs, which would also allow modeled trials of adaptation options (thinning, prescribed burning, etc.) that might change the estimated risk and increase the odds CRs would be as little affected as possible.

7. Conclusions

We have outlined a strategy to assess risk of CRs to anthropogenic climate change that involves establishing climate-related hazards from projections of climate change in response to increased greenhouse emissions during the 21st century, thus providing a science-based framework for proactive decision making in the context of climate change. In particular, we have used new and existing projections of hazards to illustrate heuristically how a CR manager can implement them in the first steps of a risk assessment to identify the probability of hazard occurrence and exposure of CRs to that hazard. Significant progress has been made in delivering climate-model data at the high spatial resolution needed by resource managers to quantify hazard distribution for a risk assessment (Lipschultz et al., 2020) (https://www.fs.usda.gov/managing-land/sc/data-dashboard), and such deliverables will continue to improve with increasing model resolution (Gutowski et al., 2020), improvements in downscaling (Lipschultz et al., 2020Pierce et al., 2014), and application of process models of hazards (Van Dusen et al., 2020).

Framing these proactive measures in the context of a risk assessment will provide a more effective means for decision makers to manage CRs for climate-change impacts through adaptation. The fact that hazards from climate change will be spatially heterogeneous as opposed to uniformly distributed makes a risk assessment particularly useful for prioritizing CRs for adaptation and management. Such efforts build on current vulnerability assessments which do not account for the probability of hazard occurrence.

Once the CRs with highest exposure are identified, the level of risk of CRs can be established according to their vulnerability characteristics, which are reasonably well established (Rockman et al., 2016Sabbioni et al., 2010Sesana et al., 2021). Depending on capacity, prioritizing these CRs for adaptation and management, however, may still require that the significance of the CR be considered in accordance with the National Historic Preservation Act and its implementing regulations. At the same time, additional insights should be sought through engagement and consultation with diverse stakeholders, including Indigenous communities, where such “multi-level decision-making can enhance a better understanding of the different values, priorities, and risk perceptions” (Fatoric and Seekamp, 2017c). Such engagement guides decision making in the implementation of adaptation measures that reduce risk by reducing exposure and vulnerability. Additional factors that affect prioritization include availability of budgetary, technical, and institutional resources needed for adaptation (Heilen et al., 2018Xiao et al., 2021). The effectiveness of adaptation, however, is limited by uncertainties in the severity and timing of climate-change impacts. Responding to climate-related risks thus requires iterative risk management that involves an ongoing process of assessment, action, monitoring, and reassessment as new information becomes available that will continue to inform adaptation measures over the long timeframes associated with future anthropogenic climate change (Jones et al., 2014).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank Polly Buotte for generously sharing her wildfire projections, John Bradford and Carl Davis for their insightful comments on an earlier draft, and the four reviewers for their suggestions that helped improve the paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

References are available at the original paper.

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fretslider
January 11, 2022 2:10 am

“ Anthropogenic climate change during the 21st century ”

That was as far as I got.

They’re preaching to the converted

AndyHce
Reply to  fretslider
January 11, 2022 2:55 am

While basing this on the idea of AGW, which is probable nonsense, the idea of specific evaluation for directing protection resources to the most vulnerable areas (also based on the value of those areas), seems like a good idea. If useful planning tools can be developed, but the money to do the development can only be obtained by relating the problem to AGW, that is just the way things work today.

fretslider
Reply to  AndyHce
January 11, 2022 3:13 am

Lets be honest, what you are attempting to describe is basic [environmental management] common sense.

Something that was abandoned in the latter half of the 20th century.

Mike Dubrasich
Reply to  AndyHce
January 11, 2022 11:39 am

Total and complete BS eco-drivel model-boggle. The errors are too numerous to list, but here are just a few:

“Cultural resources” are everything everywhere. There are no “resources” that are not cultural. Playing the Indian card is typical alarmo-prop weepy CRT crap.

USFS Region 1 (ID, MT) has been the hotbed progenitor of Let It Burn and aerial ignition — the carpet bombing of forests with incendiary devices machine-gunned from helicopters. Many R1 forests have been 90% incinerated over the last 20 years, deliberately on purpose.

The chumps who gibbered together this abomination are actually on the outside looking in. The USFS ignores such tripe from Acadumbia. The local land managers live in the communities they have all but destroyed and have some sensitivity about it. They’re trying to right the ship. Not an easy task when the Insane Left runs the Federal Government.

Mike Dubrasich
Reply to  Mike Dubrasich
January 11, 2022 12:53 pm

Cultural resources. For instance, old-growth trees. Trees over 200 yo wouldn’t exist today but for Indian burning. The indigenous residents burned the undergrowth every 3-6 years and set trees on fire purposefully to create firewood. They were careful, however, to protect a few large, mast-bearing trees (pine, oak).

That is why old-growth trees today number less than five per acre. The other 500+ stems per acre are young, mostly less than 75-100 yo.

When modern thicket forests burn, all the trees die, old and young alike. There are no “natural” fires that selective leave old trees. That doesn’t happen in nature. It only happens when human beings cause it to happen. Thus every old-growth tree is an anthropogenic cultural artifact.

Human beings lived, hunted, gathered, burned, and in many other ways modified their landscapes and watersheds for umpteen millennia. Everything: wildlife, vegetation, soils, water, rocks, et al bear the imprint of human culture. The weather is the one exception.

Richard Page
Reply to  fretslider
January 11, 2022 5:42 am

All I got from that is that they don’t think they’re getting a big enough slice of the funding pie and are positioning themselves to grab more free money when the gravy train rolls through town.

Allan MacRae
Reply to  fretslider
January 11, 2022 8:17 am

It used to be called “Global Warming”, allegedly caused by increasing atmospheric CO2 from fossil fuel combustion.. Then it stopped warming. Now it’s getting colder. So they call it “climate change”. Somewhere they lost the plot and started spouting nonsense.

MUMBAI’S COLDEST TEMP IN A DECADE; RECORD SNOW LEADS TO 38 AVALANCHES AROUND SNOQUALMIE; AND NO, THE “CLIMATE CRISIS” ISN’T CAUSING AN UPTICK IN ARCTIC LIGHTENING, COSMIC RAYS ARE
January 11, 2022 Cap Allon
Activist scientists discovered a round peg (a climatic anomaly) and have tried desperately to jam it into AGW’s decidedly square hole, offering no real scientific explanation for the phenomenon.
 
RECORD-BREAKING BLIZZARDS BATTER PAKISTAN, KILLING AT LEAST 23; EXTREME COLD HITS CANADIAN REFINERIES, SENDING GAS PRICES TO RECORD HIGHS; + HISTORIC ARCTIC BLAST HEADED FOR BOSTON, AS RECORD SNOW CONTINUES TO SWEEP U.S.
January 10, 2022 Cap Allon
Plus, NOAA’s Arctic fraud: Satellite data reveals sea ice was “significantly less” back in 1972.

Clyde Spencer
Reply to  Allan MacRae
January 11, 2022 4:53 pm

Somewhere they lost the plot and started spouting nonsense.

It was always nonsense!

Allan MacRae
Reply to  Clyde Spencer
January 11, 2022 6:23 pm

I agree with you Clyde: CAGW aka Climate Change was always a scam.
But for a few years there was some minor warming, not man-made and certainly not “catastrophic”, but that warming stopped years go – so now the warmist fraudsters have nothing with which to fool the rubes.

https://wattsupwiththat.com/2020/08/24/false-alarm-book-review/#comment-3068779
Comments on the article on Bjorn Lomborg’s latest book “HOW CLIMATE CHANGE PANIC COSTS US TRILLIONS, HURTS THE POOR, AND FAILS TO FIX THE PLANET”:
[excerpt]

There is overwhelming evidence that there never was a real human-made global warming crisis. Climate is INsensitive to increasing atmospheric CO2.
 
THE CATASTROPHIC ANTHROPOGENIC GLOBAL WARMING AND THE HUMANMADE CLIMATE CHANGE CRISES ARE PROVED FALSE
https://thsresearch.files.wordpress.com/2020/01/the-catastrophic-anthropogenic-global-warming-cagw-and-the-humanmade-climate-change-crises-are-proved-false.pdf

We have known that alleged catastrophic anthropogenic global warming was a false crisis since ~forever, that wind and solar power would not adequately replace fossil fuels and we published those conclusions in 2002. Now some of the former advocates of those false concepts are admitting their error, and others are even admitting the climate scare was always a scam, a smokescreen for their extreme-left political objectives.

1. “CLIMATE SCIENCE DOES NOT SUPPORT THE THEORY OF CATASTROPHIC HUMAN-MADE GLOBAL WARMING – THE ALLEGED WARMING CRISIS DOES NOT EXIST.”
– Sallie Baliunas, Tim Patterson and Allan MacRae 2002
See Michael Shellenberger’s 2020 confession “On Behalf Of Environmentalists, I Apologize For The Climate Scare”. https://quillette.com/2020/06/30/on-behalf-of-environmentalists-i-apologize-for-the-climate-scare/

2. “THE ULTIMATE AGENDA OF PRO-KYOTO ADVOCATES IS TO ELIMINATE FOSSIL FUELS, BUT THIS WOULD RESULT IN A CATASTROPHIC SHORTFALL IN GLOBAL ENERGY SUPPLY – THE WASTEFUL, INEFFICIENT ENERGY SOLUTIONS PROPOSED BY KYOTO ADVOCATES SIMPLY CANNOT REPLACE FOSSIL FUELS.”
– Sallie Baliunas, Tim Patterson and Allan MacRae 2002
See Michael Moore’s 2020 film “Planet of the Humans”.
https://www.youtube.com/watch?v=Zk11vI-7czE

In a July 2019 Washington Examiner report, Saikat Chakrabarti, chief of staff for Alexandria Ocasio-Cortez, said: “The interesting thing about the Green New Deal, is it wasn’t originally a climate thing at all… Do you guys think of it as a climate thing? Because we really think of it as a how-do-you-change-the-entire-economy thing.”

Naomi Klein, in her book, “On Fire, The Burning Case for a Green New Deal”, also stated that the climate is a “powerful motivator” to overthrow capitalism. “The idea is a simple one: In the process of transforming the infrastructure of our societies at the speed and scale that scientists have called for, humanity has a once-in-a- century chance to fix an economic model that is failing the majority of people on multiple fronts. … Challenging these underlying forces is an opportunity to solve several interlocking crises at once.”
The clear intent is to use the global warming scam to restrict economic and political freedoms by transforming Western countries into tightly controlled totalitarian states. That is the left’s BIG LIE, and the Democrat voters, like so many of Lenin’s “useful idiots” in the West, believe it. Human-made global warming / climate change has always been a false crisis, and its leading proponents know that – it is the greatest scam in world history.

Regards, Allan MacRae, B.A.Sc., M.Eng.

Reply to  fretslider
January 11, 2022 8:50 am

I got further, and also read:
” presents a significant challenge … ”
You wasted even less time than I did !

Paul S.
Reply to  fretslider
January 11, 2022 10:55 am

I couldn’t get through the gobuldy gook. Anyone know what they said?

Dave Fair
Reply to  Paul S.
January 11, 2022 11:44 am

1) They used UN IPCC CliSciFi models driven by RCP8.5 and downscaled. They said that as a result of that they couldn’t make “predictions,” only “projections.” Throughout the document, however, they kept making firm predictions.

2) They didn’t propose to actually do anything about current threats to government managed lands (28% of the U.S.). They just proposed more studies to prioritize different areas for further studies and classifications.

3) They said, with no actual proof, that droughts, storms, floods, wildfires & etc. are currently getting worse in the U.S. and will increasingly worsen in the future.

4) They said they did not and will not look at how management practices might affect their conclusions about prioritizing areas to manage.

5) They refused to look at arson and other ignition sources.

A typical governmental bureaucratic mass of irrelevant bullshit masquerading as a work product.

Last edited 4 months ago by Dave Fair
Martin Pinder
Reply to  fretslider
January 11, 2022 11:40 am

I got as far as ‘may be’ in the third line, then I knew I was reading speculation & not science. The expression ‘may be’ can just as well be read as ‘may not be’. Same with words like ‘could’, linked etc.

Ron Long
January 11, 2022 2:20 am

What a construction of nonsense designed to fit in with the current woke/CAGW power grabs. I have worked all over the areas described in northern Idaho and Montana, and the idea that there is an unusual risk event approaching “Cultural Resources” right now is the product of the current political science merger seeking funding to solve problems that do not exist. Sea level rise? I think Idaho and Montana are OK there. Floods and droughts? Cancel each other, never mind. Radical Eco Terrorists willing to burn or blow up things to make a point? Now there’s a problem, but in Idaho and Montana there is a known solution to this risk.

Spetzer86
Reply to  Ron Long
January 11, 2022 5:18 am

I’m guessing the methodology for dealing with Eco Terrorists has low recidivism.

Ed Zuiderwijk
January 11, 2022 2:44 am

Never mind Alaskan CRs. The belief in Anthropogenic climate change poses a serious threat to a vital cultural resource: the raw climate data. This resource is seriously endangered by the unscientific and unethical practice of ‘homogenisation’ applied to make the past appear colder than it actually was. A program to safeguard that vital cultural and scientific resource is urgently called for.

Leo Smith
January 11, 2022 2:51 am

Words fail me.

Joseph Zorzin
Reply to  Leo Smith
January 11, 2022 3:05 am

burreacratese at its finest

Joseph Zorzin
January 11, 2022 3:03 am

No wonder government agencies accomplish so little- writing mountains of BS like that- then having to try to implement it. Before anything truly useful can get done- like thin a stand of timber, the managers have to wade through that. If they sell the timber for $1,000 it’ll cost 100 times that to process the paper work and check off all the obligatories. That’s exactly what happens here in Massachusetts when the foresters working for the state try to do basic forestry on state land. I’ve been screaming about that fact for decades- but they haven’t been listening. When I talk about costs and benefits they stare back like deer in the headlights.

Clyde Spencer
Reply to  Joseph Zorzin
January 11, 2022 4:56 pm

… they stare back like deer in the headlights.

Similar IQs!

Joseph Zorzin
January 11, 2022 3:13 am

Off topic- sorry, but in today’s Boston Globe- the big story on the front page is that it’s a cold day- almost down to zero F. So, many schools have closed for the day.

“Boston Public Schools announced Monday that schools would be closed Tuesday to keep staff and students safe. ”

If the rest of America gets that stupid- we’re doomed. The story also warns people to put on gloves and hats.

When I was a kid in the ’50s in western Massachusetts, they never closed school for cold and never for snow unless it was a blizzard. Now, if there’s 2″ of snow, schools close.

fretslider
Reply to  Joseph Zorzin
January 11, 2022 3:31 am

to keep staff and students safe. ””

Break out the cotton wool.

Disputin
Reply to  fretslider
January 11, 2022 5:53 am

They are not “students“, they are pupils.

fretslider
Reply to  Disputin
January 11, 2022 10:15 am

Have you told Joseph Zorzin?

Joseph Zorzin
Reply to  Joseph Zorzin
January 11, 2022 6:06 am

In case anyone doesn’t understand that Massachusetts is probably the most climate lunatic entity on the planet, just look at what was in yesterday’s Boston Globe.

The Massachusetts gubernatorial election is just under a year away, but climate policy is already shaping up to be a key part of the race, with Democratic hopefuls issuing climate plans long before voters head to the polls.

Harvard professor and gubernatorial candidate Danielle Allen last month released an 8-page climate agenda that pledges to reach 100 percent renewable energy and decarbonization in Massachusetts by 2040, outpacing the state’s current target of net-zero emissions by 2050. In the plan, Allen also promises to divest Massachusetts’ pension fund and other public dollars from fossil fuels and halt all new fossil fuel infrastructure projects, while planning to “embed equity” into each of her climate schemes.

“The disproportionate burdens of climate change are borne by communities of color, low-income communities, and others on the frontlines of the climate crisis.” she said in a statement. “We must secure access to green jobs and a healthy environment and create a sustainable future for our children.”

Allen isn’t the only candidate making big climate promises. Days before Allen’s announcement, her leading Democratic rival State Senator Sonia Chang-Díaz released “A Green New Deal for Massachusetts.” The agenda calls to scale up renewable power and battery storage to reach a fully clean-powered energy grid by 2030, and to eliminate carbon emissions from new buildings by 2030 and all buildings by 2045.

Before bowing out of the race last week, former state Sen. Ben Downing also put out a climate agenda. It was the very first plan his campaign released.

Richard Page
Reply to  Joseph Zorzin
January 11, 2022 12:10 pm

Shades of Monty Python there with each trying to outdo the rest with ridiculous virtue signalling. Presumably the winner will be the one to announce that they will reach 100% renewables 10 years ago whilst eliminating all climate injustices forever and enabling the entire state to have more money than ever before.

Reply to  Joseph Zorzin
January 11, 2022 8:53 am

A cold day is just ‘weather”
not climate change

D. J. Hawkins
Reply to  Richard Greene
January 11, 2022 1:18 pm

Funny how when you change that adjective to “hot”, the opposite claim is trumpeted throughout the lame-stream media.

Geoff Sherrington
January 11, 2022 3:30 am

Spare a thought that the best CR examples that exist today are still there because they were sited away from past processes that destroyed their competitors. The same protection continues. Why try to improve on it?
Perhaps more cost effective to plan for detection and reduction of arson?
Geoff S

lee
January 11, 2022 3:32 am

A fine example of the five knuckle shuffle.

January 11, 2022 3:43 am

“scenarios that underlie the projections (A1B and RCP8.5) ”
RCP8.5? Say no more…
Science Fiction.

Oldseadog
January 11, 2022 3:50 am

If all the folk involved in the production of this paper had been tasked with the clearing of undergrowth in high fire risk areas instead of writing guff, I guess the risk of wildfire damage would be greatly reduced.

But of course they would not be allowed to do that sort of thing because they might upset the local wildlife.

Disputin
Reply to  Oldseadog
January 11, 2022 5:55 am

Or the bears might upset them?

Tom Halla
January 11, 2022 5:03 am

Near total gobbledegook. The real issue is bad forest and wildlands management related to sue and settle with environmentalist NGOs blocking controlled burns and logging.
Using RCP 8.5 demonstrates they are not planning for the real world.

Gregory Woods
January 11, 2022 5:42 am

The fact that climate-change hazards will be spatially heterogeneous as opposed to uniformly distributed thus makes a risk assessment particularly useful for prioritizing CRs for adaptation and management that would otherwise not be possible with a vulnerability assessment.

OK, then what?

PCman999
January 11, 2022 5:59 am

Wow, a lot of verbiage and angst spent worrying about a possible 1.5°C/century change. I’d hate to see the report they must have done for the far more urgent 10-20°C change per DAY that typically happens!

Carlo, Monte
January 11, 2022 6:03 am

So, zero forest fires in Montana before 1980.

Got it.

Jim Gorman
January 11, 2022 6:34 am

Do these folks ever, ever think to read historical documents?

An excerpt from: http://www.kansashistory.us/prairiefires.html

“Nothing of special interest occurred until we reached Diamond Springs, now in Morris County. The weather had been frosty at night and days sunny–a continuous Indian summer all the way–grass dry as powder . . . A stiff gale was blowing from the south, and when we noticed it the fire in the tall grass was roaring furiously and the flames leaping twenty feet high. Quickly we commenced firing outside our camp, whipping out the fire next to it, thereby burning a circle around it. Every man used a gunny sack or saddle blanket and worked with desperate energy. The utter destruction of the camp was imminent, and we faced the fire like men who had everything at stake. Success was ours, but the battle left scars on nearly all.”

Ecclesiastes 1:4-11

“What has been is what will be,
and what has been done is what will be done,
and there is nothing new under the sun.”

David Elstrom
January 11, 2022 6:49 am

Setting aside the fact that CO2 and water are plant food, not a threat—I fail to see how throwing the items in this risk assessment under the blanket of the climate change scam does anything except waste paper or, in the case of digital images, electricity. Just another addition to “the science” our would be masters buy with our tax dollars.

michael hart
January 11, 2022 8:08 am

“…a significant challenge to the protection of cultural resources “

Oh puleeze, spare me.

The Dark Lord
January 11, 2022 8:24 am

every single time we have tried to “protect” our “Cultural Resources” (new buzz words = trees/wildlife/waters) we have failed miserably … in fact we have usually made things worse …

Paul Johnson
January 11, 2022 8:36 am

Although this report is soaked in bureaucratese, the basic point is that the potential effects of climate change (whatever they might be) on specific locales should be addressed individually. This approach sounds like adaptation rather than mitigation. If there is potential for more severe weather, the answer is not more electric cars.

January 11, 2022 8:48 am

This so-called study is computer game nonsense!

Future warming will much more likely be good news,
just like the warming in the past 47 years.
More CO2 inb the atmosphere will continue
Greening the planet and improving
the production of food crops
that will support more human and animal life

Analyzing the opening sentence:

“Anthropogenic climate change during the 21st century presents a significant challenge … ”

There are FOUR speculations:

(1) Anthroropogenic climate change
(2) The future climate can be predicted
(3) The future climate will get worse
(4) The future climate will be a “significant challenge”

Not one of these speculations are a fact
and none resemble the past 47 years of
mild, pleasant and beneficial global warming.

This is not science.
This is climate astrology.

The grand high exalted Moderator King, Mr. Rotten,
should have his head examined
for including a computer game “study”
on this “science” website, unless he is
drunk … again. Being an optimist,
I hope the examination will find nothing.

Dave Fair
Reply to  Richard Greene
January 11, 2022 11:58 am

Richard, you are a creep for, among other things, biting the hand that feeds you.

Clyde Spencer
Reply to  Richard Greene
January 11, 2022 5:04 pm

Woe is the person who cannot see the wisdom of the saying, “Know your enemy.”

Peta of Newark
January 11, 2022 8:52 am

They’re coming thick and fast now – another flock of self-important power-trippers.

But this time trying to disguise themselves behind a blizzard of weasel words and their power massively enhanced (or so they think) by all the unassailable power that computers posses.

Get a life boys & girls, just get a life……

Olen
January 11, 2022 9:25 am

More forest fires could be assessed from the head of a match and lightning strikes.

Protecting CRs might be to assess the people who are intent on destroying our historical culture. And a little less on blaming it all on the rest of us.

Smart Rock
January 11, 2022 10:05 am

This is a good example of what bureaucrats do. They expended many thousands of words on the need to protect what they call “cultural resources” (CRs), without giving a single concrete example of a CR and how they might protect it from fire or flood.

I confess that, although I tried to read the whole thing, after a couple of pages of mind-numbing bureaucratese, my eyes started to glaze over and my attention began to wane. If any brave souls managed to wade through this verbal morass and did notice a clear-cut example of a specific CR and how to protect it, please set me right.

markl
January 11, 2022 10:31 am

“…the resulting climate changes as simulated by global climate models….” There’s been enough time to plot climate models against reality so why are they still using them for reference? Is operation money that tight that they’re willing to exchange their credibility for it? Rhetorical question.

Tom Abbott
January 11, 2022 11:26 am

From the article: “RCP8.5 scenario”

I was going to ridicule using the “RCP8.5 scenario” but now that I think of it, maybe it is becoming more valid as projections for coal use in the future skyrocket.

Although just because a lot of coal is burned doesn’t necessarily mean the Earth’s temperatures are going to rise. We may get a lot of coal use and no temperature rise.

Clarky of Oz
January 11, 2022 2:13 pm

If it ain’t broke, don’t fix it.

Clarky of Oz
January 11, 2022 2:19 pm

It was once said that if you had enough monkeys and enough typewriters, eventually one of them would produce “War and Peace.”
Still some way to go.

Clyde Spencer
Reply to  Clarky of Oz
January 11, 2022 5:08 pm

I thought that it was supposed to be Shakespeare’s collected works. I suppose with an infinite number of monkeys, one could get both.

Last edited 4 months ago by Clyde Spencer
Philip
January 11, 2022 4:30 pm

For many years now, the number and extent of forest fires can be directly traced to the “green” [tree hugger(s)] influence on policy related to govt. lands, forests, closed to private selective harvesting and seasonal burns. Where forests are properly managed, the rate and extent of fires are way below the current neglect brought about by the “tree huggers”.
They are not the people to solve this problem. It requires action. Sweat. Laborious effort. And of course, a 180 degree turn about on policy. Not the virtues found in people entrenched in the dogma of anthropogenic climate change.

Clyde Spencer
January 11, 2022 4:39 pm

RCP 8.5 is like a zombie or vampire that can’t be killed! The alarmists continue to rely on it to write scary stories.

Clyde Spencer
January 11, 2022 4:50 pm

From the Merriam-Webster dictionary:

Essential Meaning of resource

1 : something that a country has and can use to increase its wealth

// Oil is essentially their only resource.

2 : a supply of something (such as money) that someone has and can use when it is needed

// Their financial resources are severely limited. [=they do not have much money]

// If we pool our resources [=if we combine our money] we can buy the car.

3 : a place or thing that provides something useful

// The library/Internet is a useful resource [=source] for information.

// The computer laboratory is an essential resource for students.

The essence of the above definition and examples is that a resource is something that can be used. Is something a resource if all one can do is look at it?

Andy Pattullo
January 11, 2022 7:58 pm

Millions of electrons wasted on this drivel that could have warmed my coffee.

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