Downscaling Of Climate Model Outputs Research Articles

New snow metrics for a warming world

AbstractSnow is Earth's most climatically sensitive land cover type. Traditional snow metrics may not be able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE) has been an effective index for streamflow forecasting, but it cannot express the effects of midwinter melt events, now expected in warming snow climates, nor can we assume that station‐based measurements will be representative of snow conditions in future decades. Remote sensing and climate model data provide capacity for a suite of multi‐use snow metrics from local to global scales. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic or overly complicated in their interpretation. We describe a suite of spatially explicit, multi‐temporal snow metrics based on global satellite data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and downscaled climate model output for the U.S. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), At‐Risk Snow (ARS), and Frequency of a Warm Winter (FWW). Using these retrospective and prospective snow metrics, we assess the current and future snow‐related conditions in three hydroclimatically different U.S. watersheds: the Truckee, Colorado Headwaters, and Upper Connecticut. In the two western U.S. watersheds, SCF and SDD show greater sensitivity to annual differences in snow cover compared with data from the ground‐based Snow Telemetry (SNOTEL) network. The eastern U.S. watershed does not have a ground‐based network of data, so these MODIS‐derived metrics provide uniquely valuable snow information. The ARS and FWW metrics show that the Truckee Watershed is highly vulnerable to conversion from snowfall to rainfall (ARS) and midwinter melt events (FWW) throughout the seasonal snow zone. In comparison, the Colorado Headwaters and Upper Connecticut Watersheds are colder and much less vulnerable through mid‐ and late‐century.

Multisite Generalization of the SHArP Weather Generator

AbstractGeneralization of point-scale stochastic weather generators to simultaneously produce output at multiple sites provides more powerful support for hydrology and climate change impact studies. Generalization preserves the statistical properties of each individual site while maintaining proper spatial correlation over the domain. Here, generalization of the daily precipitation and temperature components of the stochastic harmonic autoregressive parametric (SHArP) weather generator is presented. The generalization process for temperature involves conversion of vector time series to matrix time series that capture between-site covariances of maximum and minimum daily temperature. Between-site temperature covariances depend on spatial precipitation-occurrence patterns (POPs), of which there are up to 2M for M sites. To dramatically reduce the number of covariance matrices that drive temperature, multisite SHArP uses empirical orthogonal function analysis to categorize the POPs and harmonic smoothing to reduce the number of parameters describing the temporal evolution (annual cycle) of the elements in the covariance matrices. By modeling precipitation-regime-specific residuals, the model is shown to capture statistically significant spatial and temporal contrasts in observed temperature covariance. For precipitation simulation, we extend existing techniques by adding a trend term to the occurrence and amount parameters. Multisite generalization of the framework is illustrated by simulating stochastic historical and future temperature and precipitation across complex terrain over northern Utah on the basis of historical station observations and historical and future statistically downscaled climate model output.

Simulated Sensitivity of Urban Green Infrastructure Practices to Climate Change.

Climate change is likely to alter the quantity and quality of urban stormwater, presenting a risk to water quality and public health. How might stormwater management practices need to change to address future climate? Answering requires understanding how management practices respond to climate forcing. Traditional "gray" stormwater design employs engineered structures, sized based on assumptions about future rainfall, which have limited flexibility once built. Green infrastructure (GI) uses vegetation, soil, and distributed structures to manage rainwater where it falls and may provide greater flexibility for adaptation. There is, however, uncertainty about how a warmer climate may affect performance of different types of GI. This study uses the hydrologic and biogeochemical watershed model, Regional Hydro-Ecologic Simulation System (RHESSys), to investigate sensitivity of different GI practices to climate. Simulations examine 36 urban "archetypes" representing different development patterns (at the city block scale) of typical U.S. cities, eleven regional climatic settings, and a range of mid-21st century scenarios based on downscaled climate model output. Results suggest regionally variable effects of climate change on the performance of GI practices for water quantity, water quality, and carbon sequestration. GI is able to mitigate most projected future increases in surface runoff, while bioretention can mitigate increased nitrogen yield at nine of eleven sites. Simulated changes in carbon balance are small, while local evaporative cooling can be substantial. Given uncertainty in the local expression of future climate, infrastructure design should emphasize flexibility and robustness to a range of future conditions.

Projected climate change impacts in rainfall erosivity over Brazil

The impacts of climate change on soil erosion may bring serious economic, social and environmental problems. However, few studies have investigated these impacts on continental scales. Here we assessed the influence of climate change on rainfall erosivity across Brazil. We used observed rainfall data and downscaled climate model output based on Hadley Center Global Environment Model version 2 (HadGEM2-ES) and Model for Interdisciplinary Research On Climate version 5 (MIROC5), forced by Representative Concentration Pathway 4.5 and 8.5, to estimate and map rainfall erosivity and its projected changes across Brazil. We estimated mean values of 10,437 mm ha−1 h−1 year−1 for observed data (1980–2013) and 10,089 MJ mm ha−1 h−1 year−1 and 10,585 MJ mm ha−1 h−1 year−1 for HadGEM2-ES and MIROC5, respectively (1961–2005). Our analysis suggests that the most affected regions, with projected rainfall erosivity increases ranging up to 109% in the period 2007–2040, are northeastern and southern Brazil. Future decreases of as much as −71% in the 2071–2099 period were estimated for the southeastern, central and northwestern parts of the country. Our results provide an overview of rainfall erosivity in Brazil that may be useful for planning soil and water conservation, and for promoting water and food security.

Designing ecological climate change impact assessments to reflect key climatic drivers.

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.

Peer review report 1 On “Hydrological response to dynamical downscaling of climate model outputs: a case study for western and eastern snowmelt-dominated Canada catchments”

Peer review report 1 On “Hydrological response to dynamical downscaling of climate model outputs: a case study for western and eastern snowmelt-dominated Canada catchments”

A 133-Year Record of Climate Change and Variability from Sheffield, England

A 133-year length (1883–2015) daily climate record from Sheffield, England (53.38°N, 1.49°W) is analysed. Across the entire length of the record, there are significant warming trends annually and for all seasons, whereas precipitation shows a significant annual increase but the seasonal trends, whilst all positive, are not significant. Trends in extreme indices mirror the mean long-term warming and wetting signal. Record hot and cold daily temperatures and precipitation amounts are associated with summer anticyclonic conditions, an anomalous easterly winter jet stream and summer cyclonic activity, respectively. Whilst there are large uncertainties surrounding the calculation of return periods for the daily maximum, minimum and precipitation records from a single record, our best estimates suggest that in the current climate (2015), the existing records have return periods of 38, 529 and 252 years, respectively. The influence of several climate indices on mean and extreme indices are considered on seasonal scales, with the North Atlantic Oscillation displaying the strongest relationship. Future mean maximum temperature and precipitation alongside extreme indices representing the warmest and wettest day of the year are analysed from two downscaled climate model output archives under analysis periods of a 1.5 and 2 degree warmer world and the 2080–2099 end of 21st century period. For this mid-latitude location, there is minimal difference in model projections between a 1.5 and 2 degree world, but a significant difference between the 1.5/2 degree world and the end of century 2080–2099 period under the most severe climate warming scenarios.

Assessing distribution‐based climate model bias correction methods over an alpine domain: added value and limitations

ABSTRACTClimate impact assessment and decision making in the light of projected future climate change require accurate and robust climate scenarios at the local scale. The latter are targeted by statistical bias correction (BC) and downscaling of climate model output. A nowadays well‐established technique is quantile mapping (QM). Here, we apply several different implementations of empirical QM, a parametric (and computationally more expensive) QM method for daily precipitation, and a mean‐BC to an ensemble of regional climate scenarios over the topographically structured terrain of Switzerland. The performance of these methods in the current climate and their long‐term stability are analysed with respect to distributional and temporal statistics as well as climate impact indices for daily temperature and precipitation. We select an optimal QM implementation, study its effect on the inter‐variable consistency, and compare its climate change signal (CCS) to that of a delta‐change (DC) method. The results demonstrate that QM effectively reduces raw model biases. The most important improvements are corrected magnitudes and, for precipitation, also wet‐day frequencies. The quantile‐dependent bias removal is superior to the mean‐BC with respect to distribution‐tail statistics. Temporal statistics and climate impact indices are also improved. There is no performance benefit from the parametric QM method. The selected empirical QM implementation substantially improves the joint temperature–precipitation distribution and maintains the temperature–precipitation cross‐correlation function as represented by raw climate model data. The CCS analysis reveals the superiority of QM over the DC method with respect to distribution‐tail characteristics and temporal statistics. This work reveals empirical QM as a reliable and stable method for BC and downscaling from state‐of‐the‐art regional climate models to local weather stations over alpine terrain, confirming and expanding previous studies.

Climate Change Implications for Tropical Islands: Interpolating and Interpreting Statistically Downscaled GCM Projections for Management and Planning

AbstractThe potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to decline in both model ensembles, but the decrease was more severe in the “regionally consistent” models. The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960–90 period to the 2071–99 period was 4.6°–9°C depending on the global emission scenarios and location. This warming may cause increases in CDD, and consequently increasing energy demands. Life zones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.

Hydrological response to dynamical downscaling of climate model outputs: A case study for western and eastern snowmelt-dominated Canada catchments

Abstract Study region An analysis of hydrological response to a dynamically downscaled multi-member multi-model global climate model (GCM) ensemble of simulations based on the Canadian Regional Climate Model (CRCM) is presented for three snowmelt-dominated basins in Canada. The basins are situated in the western mountainous (British Columbia) and eastern level (Quebec) regions in Canada, providing comprehensive experiments to validate the CRCM over various topographic features. Study focus The evaluation of the CRCM as a tool to improve GCM simulations of catchment scale hydrology is investigated within the bounds of uncertainty associated with RCM simulations. Daily climate variables were extracted from a 30-year CRCM and GCM ensemble simulations. The hydrological response was assessed through the comparison of catchment water components simulated by SWAT. New hydrological insights for the region Results show that the CRCM captures the primary features of observed climate, but there are significant biases. Most noteworthy are a positive bias in precipitation and a negative bias in temperature over the BC basin. When looking at the hydrological modeling results, the benefit of using the RCM versus GCMs emerged distinctly for the mountainous BC basin where the RCM is preferred over the GCMs. The sensitivity experiments show that uncertainty in the GCM/RCM’s internal variability must be assessed to provide suitable regional hydrological responses to climate change.

A high‐resolution, multi‐model analysis of Irish temperatures for the mid‐21st century

ABSTRACTThere is a paucity of dynamically downscaled climate model output at a high resolution over Ireland, of temperature projections for the mid‐21st century. This study aims to address this shortcoming. A preliminary investigation of global climate model (GCM) data and high‐resolution regional climate model (RCM) data shows that the latter exhibits greater variability over Ireland by reducing the dominance of the surrounding seas on the climate signal. This motivates the subsequent dynamical downscaling and analysis of the temperature output from three high‐resolution (4–7 km grid size) RCMs over Ireland. The three RCMs, driven by four GCMs from CMIP3 and CMIP5, were run under different Special Report on Emissions Scenarios (SRES) and representative concentration pathway (RCP) future scenarios. Projections of mean and extreme temperature changes are considered for the mid‐century (2041–2060) and assessed relative to the control period of 1981–2000.Analysis of the RCM data shows that annual mean temperatures are projected to rise between 0.4 and 1.8 °C above control levels by mid‐century. On a seasonal basis, results differ by forcing scenario. Future summers have the largest projected warming under RCP 8.5, where the greatest warming is seen in the southeast of Ireland. The remaining two high emission scenarios (SRESs A1B and A2) project future winters to have the greatest warming, with almost uniform increases of 1.5–2 °C across the island.Changes in the bidecadal 5th and 95th percentile values of daily minimum and maximum temperatures, respectively, are also analysed. The greatest change in daily minimum temperature is projected for future winters (indicating fewer cold nights and frost days), a pattern that is consistent across all scenarios/forcings.An investigation into the distribution of temperature under RCP 8.5 shows a strong summer increase compounded by increased variability, and a winter increase compounded by an increase in skewness.

MODELING STREAMFLOW AND WATER QUALITY SENSITIVITY TO CLIMATE CHANGE AND URBAN DEVELOPMENT IN 20 U.S. WATERSHEDS.

Watershed modeling in 20 large, United States (U.S.) watersheds addresses gaps in our knowledge of streamflow, nutrient (nitrogen and phosphorus), and sediment loading sensitivity to mid-21st Century climate change and urban/residential development scenarios. Use of a consistent methodology facilitates regional scale comparisons across the study watersheds. Simulations use the Soil and Water Assessment Tool. Climate change scenarios are from the North American Regional Climate Change Assessment Program dynamically downscaled climate model output. Urban and residential development scenarios are from U.S. Environmental Protection Agency’s Integrated Climate and Land Use Scenarios project. Simulations provide a plausible set of streamflow and water quality responses to mid-21st Century climate change across the U.S. Simulated changes show a general pattern of decreasing streamflow volume in the central Rockies and Southwest, and increases on the East Coast and Northern Plains. Changes in pollutant loads follow a similar pattern but with increased variability. Ensemble mean results suggest that by the mid-21st Century, statistically significant changes in streamflow and total suspended solids loads (relative to baseline conditions) are possible in roughly 30-40% of study watersheds. These proportions increase to around 60% for total phosphorus and total nitrogen loads. Projected urban/residential development, and watershed responses to development, are small at the large spatial scale of modeling in this study.

Peer review report 2 On “Hydrological response to dynamical downscaling of climate model outputs: A case study for western and eastern snowmelt-dominated Canada catchments”

Peer review report 2 On “Hydrological response to dynamical downscaling of climate model outputs: A case study for western and eastern snowmelt-dominated Canada catchments”

Stochastic temporal disaggregation of monthly precipitation for regional gridded data sets

AbstractWeather generators are used for spatiotemporal downscaling of climate model outputs (e.g., precipitation and temperature) to investigate the impact of climate change on the hydrological cycle. In this study, a multiplicative random cascade model is proposed for the stochastic temporal disaggregation of monthly to daily precipitation fields, which is designed to be applicable to grids of any spatial resolution and extent. The proposed method uses stationary distribution functions that describe the partitioning of precipitation throughout multiple temporal scales (e.g., weekly and biweekly scale). Moreover, it explicitly considers the intensity and spatial covariance of precipitation in the disaggregation procedure, but requires no assumption about the temporal relationship and spatial isotropy of precipitation fields. A split sampling test is conducted on a high‐resolution (i.e., 4 × 4 km2 grid) daily precipitation data set over Germany (≈357,000 km2) to assess the performance of the proposed method during future periods. The proposed method has proven to consistently reproduce distinctive location‐dependent precipitation distribution functions with biases less than 5% during both a calibration and evaluation period. Furthermore, extreme precipitation amounts and the spatial and temporal covariance of the generated fields are comparable to those of the observations. Consequently, the proposed temporal disaggregation approach satisfies the minimum conditions for a precipitation generator aiming at the assessment of hydrological response to climate change at regional and continental scales or for generating seamless predictions of hydrological variables.

Influence of climate on landscape characteristics in safety assessments of repositories for radioactive wastes

In safety assessments of repositories for radioactive wastes, large spatial and temporal scales have to be considered when developing an approach to risk calculations. A wide range of different types of information may be required. Local to the site of interest, temperature and precipitation data may be used to determine the erosional regime (which may also be conditioned by the vegetation characteristics adopted, based both on climatic and other considerations). However, geomorphological changes may be governed by regional rather than local considerations, e.g. alteration of river base levels, river capture and drainage network reorganisation, or the progression of an ice sheet or valley glacier across the site. The regional climate is in turn governed by the global climate.In this work, a commentary is presented on the types of climate models that can be used to develop projections of climate change for use in post-closure radiological impact assessments of geological repositories for radioactive wastes. These models include both Atmosphere-Ocean General Circulation Models and Earth Models of Intermediate Complexity. The relevant outputs available from these models are identified and consideration is given to how these outputs may be used to inform projections of landscape development. Issues of spatial and temporal downscaling of climate model outputs to meet the requirements of local-scale landscape development modelling are also addressed. An example is given of how climate change and landscape development influence the radiological impact of radionuclides potentially released from the deep geological disposal facility for spent nuclear fuel that SKB (the Swedish Nuclear Fuel and Waste Management Company) proposes to construct at Forsmark, Sweden.

An Enhanced Archive Facilitating Climate Impacts and Adaptation Analysis

We describe the expansion of a publicly available archive of downscaled climate and hydrology projections for the United States. Those studying or planning to adapt to future climate impacts demand downscaled climate model output for local or regional use. The archive we describe attempts to fulfill this need by providing data in several formats, selectable to meet user needs. Our archive has served as a resource for climate impacts modelers, water managers, educators, and others. Over 1,400 individuals have transferred more than 50 TB of data from the archive. In response to user demands, the archive has expanded from monthly downscaled data to include daily data to facilitate investigations of phenomena sensitive to daily to monthly temperature and precipitation, including extremes in these quantities. New developments include downscaled output from the new Coupled Model Intercomparison Project phase 5 (CMIP5) climate model simulations at both the monthly and daily time scales, as well as simulations of surface hydrologi- cal variables. The web interface allows the extraction of individual projections or ensemble statistics for user-defined regions, promoting the rapid assessment of model consensus and uncertainty for future projections of precipitation, temperature, and hydrology. The archive is accessible online (http://gdo-dcp.ucllnl.org/downscaled_ cmip_projections).

The role of regional climate projections in managing complex socio-ecological systems

Climate is one of many factors to be considered in adapting systems to environmental and societal change and often it is not the most important factor. Moreover, given considerable model inadequacies, irreducible uncertainties, and poor accessibility to model output, we may legitimately ask whether or not regional climate projections ought to have a central role in guiding climate change adaptation decisions. This question is addressed by analysing the value of regional downscaled climate model output in the management of complex socio-ecological systems (SESs) vulnerable to climate change. We demonstrate, using the example of the Dwesa–Cwebe region in South Africa, that the management of such systems under changing environmental and socio-economic conditions requires a nuanced and holistic approach that addresses cross-scale system interdependencies and incorporates “complexity thinking”. We argue that the persistent focus on increasing precision and skill in regional climate projections is misguided and does not adequately address the needs of society. However, this does not imply that decision makers should exclude current and future generations of regional climate projections in their management processes. On the contrary, ignoring such information, however uncertain and incomplete, risks the implementation of maladaptive policies and practices. By using regional climate projections to further explore uncertainties and investigate cross-scale system dependencies, such information can be used to aid understanding of how SESs might evolve under alternative future societal and environmental scenarios.

Southern African Monthly Rainfall Variability: An Analysis Based on Generalized Linear Models

Southern Africa is characterized by a high degree of rainfall variability, affecting agriculture and hydrology, among other sectors. This paper aims to investigate such variability and to identify stable relationships with its potential drivers in the climate system; such relationships may be used as the basis for the statistical downscaling of climate model outputs, for example. The analysis uses generalized linear models (GLMs). The GLMs are fitted to twentieth-century observational data for the period 1957–2006 to characterize the dependence of monthly precipitation occurrences and amounts upon the climate indicators of interest. In contrast with many of the analyses that have previously been used to investigate controls on precipitation in the region, GLMs allow for the investigation of the relationships between different components of the climate system (geographical and climatic drivers) simultaneously. Six climate factors were found to drive part of the rainfall variability in the region, and their modeled effect upon rainfall occurrences and amounts resulted in general agreement with previous studies. Among the retained indices, relative humidity and El Niño accounted for the highest degree of explained variability. The location and intensity of the jet stream were also found to have a statistically significant and physically meaningful effect upon rainfall variability.

Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change

Abstract The impacts of climate change are expected to be generally detrimental for agriculture in many parts of Africa. Overall, warming and drying may reduce crop yields by 10–20% to 2050, but there are places where losses are likely to be much more severe. Increasing frequencies of heat stress, drought and flooding events will result in yet further deleterious effects on crop and livestock productivity. There will be places in the coming decades where the livelihood strategies of rural people may need to change, to preserve food security and provide income-generating options. These are likely to include areas of Africa that are already marginal for crop production; as these become increasingly marginal, then livestock may provide an alternative to cropping. We carried out some analysis to identify areas in sub-Saharan Africa where such transitions might occur. For the currently cropped areas (which already include the highland areas where cropping intensity may increase in the future), we estimated probabilities of failed seasons for current climate conditions, and compared these with estimates obtained for future climate conditions in 2050, using downscaled climate model output for a higher and a lower greenhouse-gas emission scenario. Transition zones can be identified where the increased probabilities of failed seasons may induce shifts from cropping to increased dependence on livestock. These zones are characterised in terms of existing agricultural system, current livestock densities, and levels of poverty. The analysis provides further evidence that climate change impacts in the marginal cropping lands may be severe, where poverty rates are already high. Results also suggest that those likely to be more affected are already more poor, on average. We discuss the implications of these results in a research-for-development targeting context that is likely to see the poor disproportionately and negatively affected by climate change.

Hydrological responses to dynamically and statistically downscaled climate model output

Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) re‐analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic simulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of downscaling technique, and point to the need for caution when interpreting future hydrological scenarios.