The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101803/1/eost2013EO460005.pd

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed

D.: Global Atmospheric Sensitivity to Tropical SST Anomalies throughout the IndoPacific Basin

ABSTRACT The sensitivity of the global atmospheric response to sea surface temperature (SST) anomalies throughout the tropical Indian and Pacific Ocean basins is investigated using the NCEP MRF9 general circulation model (GCM). Model responses in January are first determined for a uniform array of 42 localized SST anomaly patches over the domain. Results from the individual forcing experiments are then linearly combined using a statistically based smoothing procedure to produce sensitivity maps for many target quantities of interest, including the geopotential height response over the Pacific-North American (PNA) region and regional precipitation responses over North America, South America, Africa, Australia, and Indonesia. Perhaps the most striking result from this analysis is that many important targets for seasonal forecasting, including the PNA response, are most sensitive to SST anomalies in the Niño-4 region (5ЊN-5ЊS, 150ЊW-160ЊE) of the central tropical Pacific, with lesser and sometimes opposite sensitivities to SST anomalies in the Niño-3 region (5ЊN-5ЊS, 90Њ-150ЊW) of the eastern tropical Pacific. However, certain important targets, such as Indonesian rainfall, are most sensitive to SST anomalies outside both the Niño-4 and -3 regions. These results are also relevant in assessing atmospheric sensitivity to changes in tropical SSTs on decadal to centennial scales associated with natural as well as anthropogenic forcing. In this context it is interesting to note the surprising result that warm SST anomalies in one-third of the Indo-Pacific domain lead to a decrease of global mean precipitation

Projections of Mountain Snowpack Loss for Wolverine Denning Elevations in the Rocky Mountains

Abstract Future reduction in mountain snowpack due to anthropogenic climate change poses a threat to many snow‐adapted species worldwide. Mountain topography exerts a strong control on snowpack not only due to elevation but also through the effect of slope and aspect on the surface energy balance. We develop high‐resolution projections of snowpack in order to provide improved, physically based estimates of the spatial distribution of future snowpack to inform species conservation efforts for the wolverine (Gulo gulo) in two study areas in the Rocky Mountains: one in Montana with known den sites and one in Colorado with recent wolverine activity and potential for reintroduction. Here we assess springtime snowpack loss in actual and potential denning areas under five future climate scenarios for the mid‐21st century. Snowpack in April and May is likely to persist into the mid‐21st century in the upper half of current denning elevations in all but the warmest future climate scenario, while large declines are projected for the lower half of the denning elevations. We gain new insight into the influence of topographical aspect on future snowpack and quantify the potential for enhanced snow persistence on north and east facing slopes under future scenarios that is only revealed in simulations where terrain slopes are resolved

Catchment Response to Bark Beetle Outbreak and Dust-on-Snow in the Colorado Rocky Mountains

Since 2002, the headwaters of the Colorado River and nearby basins have experienced extensive changes in land cover at sub-annual timescales. Widespread tree mortality from bark beetle infestation has taken place across a range of forest types, elevation, and latitude. Extent and severity of forest structure alteration have been observed through a combination of aerial survey, satellite remote-sensing, and in situ measurements. Additional perturbations have resulted from deposition of dust from regional dry-land sources on mountain snowpacks that strongly alter the snow surface albedo, driving earlier and faster snowmelt runoff. One challenge facing past studies of these forms of disturbance is the relatively small magnitude of the disturbance signals within the larger climatic signal. The combined impacts of forest disturbance and dust-on-snow are explored within a hydrologic modeling framework. We drive the Distributed Hydrology Soil and Vegetation Model (DHSVM) with observed meteorological data, time-varying maps of leaf area index and forest properties to emulate bark beetle impacts, and parameterizations of snow albedo based on observations of dust forcing. Results from beetle-killed canopy alteration suggest slightly greater snow accumulation as a result of less interception and reduced canopy sublimation and evapotranspiration, contributing to overall increases in annual water yield between 8% and 13%. However, understory regeneration roughly halves the changes in water yield. A purely observation-based estimate of runoff coefficient change with cumulative forest mortality shows comparable sensitivities to simulated results; however, positive water yield changes are not statistically significant (p ⩽ 0.05). The primary hydrologic impact of dust-on-snow forcing is an increased rate of snowmelt associated with more extreme dust deposition, producing earlier peak streamflow rates on the order of 1–3 weeks. Simulations of combined bark beetle and dust-on-snow produced little compounding effects, due to the relatively exclusive nature of their impacts. Potential changes in water yield and peak streamflow timing have important implications for regional water management decisions

Historical mean E₀ across the different E₀ formulations.

<p>Climatological mean E₀ (mm) across the CONUS for the MJJAS period in GFDL-ESM2M (first three plots in left column) and CanESM2 (right column) as estimated by the Penman-Monteith (first row), Hargreaves-Samani (second row), and Priestley-Taylor (third row) formulations for the 1976–2005 period. The bottom left plot shows observed mean MJJAS pan evaporation across the CONUS from 228 stations which had at least 20 years of data between 1950 and 2001.</p

Drought risk assessment under climate change is sensitive to methodological choices for the estimation of evaporative demand

<div><p>Several studies have projected increases in drought severity, extent and duration in many parts of the world under climate change. We examine sources of uncertainty arising from the methodological choices for the assessment of future drought risk in the continental US (CONUS). One such uncertainty is in the climate models’ expression of evaporative demand (E₀), which is not a direct climate model output but has been traditionally estimated using several different formulations. Here we analyze daily output from two CMIP5 GCMs to evaluate how differences in E₀ formulation, treatment of meteorological driving data, choice of GCM, and standardization of time series influence the estimation of E₀. These methodological choices yield different assessments of spatio-temporal variability in E₀ and different trends in 21^st century drought risk. First, we estimate E₀ using three widely used E₀ formulations: Penman-Monteith; Hargreaves-Samani; and Priestley-Taylor. Our analysis, which primarily focuses on the May-September warm-season period, shows that E₀ climatology and its spatial pattern differ substantially between these three formulations. Overall, we find higher magnitudes of E₀ and its interannual variability using Penman-Monteith, in particular for regions like the Great Plains and southwestern US where E₀ is strongly influenced by variations in wind and relative humidity. When examining projected changes in E₀ during the 21^st century, there are also large differences among the three formulations, particularly the Penman-Monteith relative to the other two formulations. The 21^st century E₀ trends, particularly in percent change and standardized anomalies of E₀, are found to be sensitive to the long-term mean value and the amplitude of interannual variability, i.e. if the magnitude of E₀ and its interannual variability are relatively low for a particular E₀ formulation, then the normalized or standardized 21^st century trend based on that formulation is amplified relative to other formulations. This is the case for the use of Hargreaves-Samani and Priestley-Taylor, where future E₀ trends are comparatively much larger than for Penman-Monteith. When comparing Penman-Monteith E₀ responses between different choices of input variables related to wind speed, surface roughness, and net radiation, we found differences in E₀ trends, although these choices had a much smaller influence on E₀ trends than did the E₀ formulation choices. These methodological choices and specific climate model selection, also have a large influence on the estimation of trends in standardized drought indices used for drought assessment operationally. We find that standardization tends to amplify divergences between the E₀ trends calculated using different E₀ formulations, because standardization is sensitive to both the climatology and amplitude of interannual variability of E₀. For different methodological choices and GCM output considered in estimating E₀, we examine potential sources of uncertainty in 21^st century trends in the Standardized Precipitation Evapotranspiration Index (SPEI) and Evaporative Demand Drought Index (EDDI) over selected regions of the CONUS to demonstrate the practical implications of these methodological choices for the quantification of drought risk under climate change.</p></div