Preparing for climate change in Washington State

Climate change is expected to bring potentially significant changes to Washington State’s natural, institutional, cultural, and economic landscape. Addressing climate change impacts will require a sustained commitment to integrating climate information into the day-to-day governance and management of infrastructure, programs, and services that may be affected by climate change. This paper discusses fundamental concepts for planning for climate change and identifies options for adapting to the climate impacts evaluated in the Washington Climate Change Impacts Assessment. Additionally, the paper highlights potential avenues for increasing flexibility in the policies and regulations used to govern human and natural systems in Washington.     

Implications of 21st century climate change for the hydrology of Washington State

Pacific Northwest (PNW) hydrology is particularly sensitive to changes in climate because snowmelt dominates seasonal runoff, and temperature changes impact the rain/snow balance. Based on results from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4), we updated previous studies of implications of climate change on PNW hydrology. PNW 21st century hydrology was simulated using 20 Global Climate Models (GCMs) and 2 greenhouse gas emissions scenarios over Washington and the greater Columbia River watershed, with additional focus on the Yakima River watershed and the Puget Sound which are particularly sensitive to climate change. We evaluated projected changes in snow water equivalent (SWE), soil moisture, runoff, and streamflow for A1B and B1 emissions scenarios for the 2020s, 2040s, and 2080s. April 1 SWE is projected to decrease by approximately 38–46% by the 2040s (compared with the mean over water years 1917–2006), based on composite scenarios of B1 and A1B, respectively, which represent average effects of all climate models. In three relatively warm transient watersheds west of the Cascade crest, April 1 SWE is projected to almost completely disappear by the 2080s. By the 2080s, seasonal streamflow timing will shift significantly in both snowmelt dominant and rain–snow mixed watersheds. Annual runoff across the State is projected to increase by 2–3% by the 2040s; these changes are mainly driven by projected increases in winter precipitation.     

Climate change in the northeastern US: regional climate model validation and climate change projections

A high resolution regional climate model (RCM) is used to simulate climate of the recent past and to project future climate change across the northeastern US. Different types of uncertainties in climate simulations are examined by driving the RCM with different boundary data, applying different emissions scenarios, and running an ensemble of simulations with different initial conditions. Empirical orthogonal functions analysis and K-means clustering analysis are applied to divide the northeastern US region into four climatologically different zones based on the surface air temperature (SAT) and precipitation variability. The RCM simulations tend to overestimate SAT, especially over the northern part of the domain in winter and over the western part in summer. Statistically significant increases in seasonal SAT under both higher and lower emissions scenarios over the whole RCM domain suggest the robustness of future warming. Most parts of the northeastern US region will experience increasing winter precipitation and decreasing summer precipitation, though the changes are not statistically significant. The greater magnitude of the projected temperature increase by the end of the twenty-first century under the higher emissions scenario emphasizes the essential role of emissions choices in determining the potential future climate change.     

Precipitation extremes and the impacts of climate change on stormwater infrastructure in Washington State

The design of stormwater infrastructure is based on an underlying assumption that the probability distribution of precipitation extremes is statistically stationary. This assumption is called into question by climate change, resulting in uncertainty about the future performance of systems constructed under this paradigm. We therefore examined both historical precipitation records and simulations of future rainfall to evaluate past and prospective changes in the probability distributions of precipitation extremes across Washington State. Our historical analyses were based on hourly precipitation records for the time period 1949–2007 from weather stations in and near the state’s three major metropolitan areas: the Puget Sound region, Vancouver (WA), and Spokane. Changes in future precipitation were evaluated using two runs of the Weather Research and Forecast (WRF) regional climate model (RCM) for the time periods 1970–2000 and 2020–2050, dynamically downscaled from the ECHAM5 and CCSM3 global climate models. Bias-corrected and statistically downscaled hourly precipitation sequences were then used as input to the HSPF hydrologic model to simulate streamflow in two urban watersheds in central Puget Sound. Few statistically significant changes were observed in the historical records, with the possible exception of the Puget Sound region. Although RCM simulations generally predict increases in extreme rainfall magnitudes, the range of these projections is too large at present to provide a basis for engineering design, and can only be narrowed through consideration of a larger sample of simulated climate data. Nonetheless, the evidence suggests that drainage infrastructure designed using mid-20th century rainfall records may be subject to a future rainfall regime that differs from current design standards.     

Future extreme events in European climate: an exploration of regional climate model projections

This paper compares how well satellite versus weather station measurements of climate predict agricultural performance in Brazil, India, and the United States. Although weather stations give accurate measures of ground conditions, they entail sporadic observations that require interpolation where observations are missing. In contrast, satellites have trouble measuring some ground phenomenon such as precipitation but they provide complete spatial coverage of various parameters over a landscape. The satellite temperature measurements slightly outperform the interpolated ground station data but the precipitation ground measurements generally outperform the satellite surface wetness index. In India, the surface wetness index outperforms station precipitation but this may be reflecting irrigation, not climate. The results suggest that satellites provide promising measures of temperature but that ground station data may still be preferred for measuring precipitation in rural settings.     

Climate change projections for Greek viticulture as simulated by a regional climate model

Theoretical and Applied Climatology - Viticulture represents an important economic activity for Greek agriculture. Winegrapes are cultivated in many areas covering the whole Greek territory, due to...     

Regional climate model sensitivity to domain size

Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere.     

Regional climate simulations over Vietnam using the WRF model

Time-irreversible symmetry is a fundamental property of nonlinear time series. Time-irreversible behaviors of mean temperature measured on 182 stations over China from 1960 to 2012 are analyzed by directed horizontal visibility graph (DHVG for short), and significance of results has been estimated by Monte Carlo simulations. It is found that dominated time irreversibility emerges in nearly all daily temperature anomaly variations over China. Further studies indicate that these time-irreversible behaviors result from asymmetric distributions of persistent daily temperature increments and decrements, and this kind of symmetry can be quantified by distributions of consecutive daily mean temperature increasing or decreasing steps. At the same time, the findings above have been confirmed by artificially generated time series with given value of multiscale asymmetry.     

An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections

Ten regional climate models (RCM) have been integrated with the standard forcings of the PRUDENCE experiment: IPCC-SRES A2 radiative forcing and Hadley Centre boundary conditions. The response over Europe, calculated as the difference between the 2071–2100 and the 1961–1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance in eight sub-European boxes. Four sources of uncertainty can be evaluated with the material provided by the PRUDENCE project. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30). Model uncertainty is due to the fact that the models use different techniques to discretize the equations and to represent sub-grid effects. Radiative uncertainty is due to the fact that IPCC-SRES A2 is merely one hypothesis. Some RCMs have been run with another scenario of greenhouse gas concentration (IPCC-SRES B2). Boundary uncertainty is due to the fact that the regional models have been run under the constraint of the same global model. Some RCMs have been run with other boundary forcings. The contribution of the different sources varies according to the field, the region and the season, but the role of boundary forcing is generally greater than the role of the RCM, in particular for temperature. Maps of minimum expected 2m temperature and precipitation responses for the IPCC-A2 scenario show that, despite the above mentioned uncertainties, the signal from the PRUDENCE ensemble is significant.     

Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific

Regional climate projections in the Pacific region are potentially sensitive to a range of existing model biases. This study examines the implications of coupled model biases on regional climate projections in the tropical western Pacific. Model biases appear in the simulation of the El Niño Southern Oscillation, the location and movement of the South Pacific Convergence Zone, rainfall patterns, and the mean state of the ocean–atmosphere system including the cold tongue bias and erroneous location of the edge of the Western Pacific warm pool. These biases are examined in the CMIP3 20th century climate models and used to provide some context to the uncertainty in interpretations of regional-scale climate projections for the 21st century. To demonstrate, we provide examples for two island nations that are located in different climate zones and so are affected by different biases: Nauru and Palau. We discuss some of the common approaches to analyze climate projections and whether they are effective in reducing the effect of model biases. These approaches include model selection, calculating multi model means, downscaling and bias correcting.     

Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models

A large component of present-day sea-level rise is due to the melt of glaciers other than the ice sheets. Recent projections of their contribution to global sea-level rise for the twenty-first century range between 70 and 180 mm, but bear significant uncertainty due to poor glacier inventory and lack of hypsometric data. Here, we aim to update the projections and improve quantification of their uncertainties by using a recently released global inventory containing outlines of almost every glacier in the world. We model volume change for each glacier in response to transient spatially-differentiated temperature and precipitation projections from 14 global climate models with two emission scenarios (RCP4.5 and RCP8.5) prepared for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The multi-model mean suggests sea-level rise of 155 ± 41 mm (RCP4.5) and 216 ± 44 mm (RCP8.5) over the period 2006–2100, reducing the current global glacier volume by 29 or 41 %. The largest contributors to projected global volume loss are the glaciers in the Canadian and Russian Arctic, Alaska, and glaciers peripheral to the Antarctic and Greenland ice sheets. Although small contributors to global volume loss, glaciers in Central Europe, low-latitude South America, Caucasus, North Asia, and Western Canada and US are projected to lose more than 80 % of their volume by 2100. However, large uncertainties in the projections remain due to the choice of global climate model and emission scenario. With a series of sensitivity tests we quantify additional uncertainties due to the calibration of our model with sparsely observed glacier mass changes. This gives an upper bound for the uncertainty range of ±84 mm sea-level rise by 2100 for each projection.     

Assessment of CMIP5 global model simulations and climate change projections for the 21 st century using a modified Thornthwaite climate classification

A modified Thornthwaite Climate Classification is applied to a 32-member ensemble of CMIP5 GCMs in order to 1) evaluate model performance in the historical climate and 2) assess projected climate change at the end of the 21 ^{s t} century following two greenhouse gas representative concentration pathways (RCP4.5 and RCP8.5). This classification scheme differs from the well-known Köppen approach as it uses potential evapotranspiration for thermal conditions, a moisture index for moisture conditions, and has even intervals between climate classes. The multi-model ensemble (MME) reproduces the main spatial features of the global climate reasonably well, however, in many regions the climate types are too moist. Extreme climate types, such as those found in polar and desert regions, as well as the cool- and cold-wet types of eastern North America and the warm and cool-moist types found in the southern U.S., eastern South America, central Africa and Europe are reproduced best by the MME. In contrast, the cold-dry and cold-semiarid climate types characterizing much of the high northern latitudes and the warm-wet type found in parts of Indonesia and southeast Asia are poorly represented by the MME. Regionally, most models exhibit the same sign in moisture and thermal biases, varying only in magnitude. Substantial changes in climate types are projected in both the RCP4.5 and RCP8.5 scenarios. Area coverage of torrid climate types expands by 11 % and 19 % in the RCP4.5 and RCP8.5 projections, respectively. Furthermore, a large portion of these areas in the tropics will experience thermal conditions which exceed the range of historical values and fall into a novel super torrid climate class. The greatest growth in moisture types in climate zones is among those with dry climates (moisture index values < 0) with increased areas of more than 8 % projected by the RCP8.5 MME.     

Climate change in Central America and Mexico: regional climate model validation and climate change projections

Central America has high biodiversity, it harbors high-value ecosystems and it??s important to provide regional climate change information to assist in adaptation and mitigation work in the region. Here we study climate change projections for Central America and Mexico using a regional climate model. The model evaluation shows its success in simulating spatial and temporal variability of temperature and precipitation and also in capturing regional climate features such as the bimodal annual cycle of precipitation and the Caribbean low-level jet. A variety of climate regimes within the model domain are also better identified in the regional model simulation due to improved resolution of topographic features. Although, the model suffers from large precipitation biases, it shows improvements over the coarse-resolution driving model in simulating precipitation amounts. The model shows a dry bias in the wet season and a wet bias in the dry season suggesting that it??s unable to capture the full range of precipitation variability. Projected warming under the A2 scenario is higher in the wet season than that in the dry season with the Yucatan Peninsula experiencing highest warming. A large reduction in precipitation in the wet season is projected for the region, whereas parts of Central America that receive a considerable amount of moisture in the form of orographic precipitation show significant decreases in precipitation in the dry season. Projected climatic changes can have detrimental impacts on biodiversity as they are spatially similar, but far greater in magnitude, than those observed during the El Ni?o events in recent decades that adversely affected species in the region.     

Probabilistic projections of transient climate change

This paper describes a Bayesian methodology for prediction of multivariate probability distribution functions (PDFs) for transient regional climate change. The approach is based upon PDFs for the equilibrium response to doubled carbon dioxide, derived from a comprehensive sampling of uncertainties in modelling of surface and atmospheric processes, and constrained by multiannual mean observations of recent climate. These PDFs are sampled and scaled by global mean temperature predicted by a Simple Climate Model (SCM), in order to emulate corresponding transient responses. The sampled projections are then reweighted, based upon the likelihood that they correctly replicate observed historical changes in surface temperature, and combined to provide PDFs for 20 year averages of regional temperature and precipitation changes to the end of the twenty-first century, for the A1B emissions scenario. The PDFs also account for modelling uncertainties associated with aerosol forcing, ocean heat uptake and the terrestrial carbon cycle, sampled using SCM configurations calibrated to the response of perturbed physics ensembles generated using the Hadley Centre climate model HadCM3, and other international climate model simulations. Weighting the projections using observational metrics of recent mean climate is found to be as effective at constraining the future transient response as metrics based on historical trends. The spread in global temperature response due to modelling uncertainty in the carbon cycle feedbacks is determined to be about 65–80 % of the spread arising from uncertainties in modelling atmospheric, oceanic and aerosol processes of the climate system. Early twenty-first century aerosol forcing is found to be extremely unlikely to be less than ?1.7 W m^?2. Our technique provides a rigorous and formal method of combining several lines of evidence used in the previous IPCC expert assessment of the Transient Climate Response. The 10th, 50th and 90th percentiles of our observationally constrained PDF for the Transient Climate Response are 1.6, 2.0 and 2.4 °C respectively, compared with the 10–90 % range of 1.0–3.0 °C assessed by the IPCC.     

Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.     

Effects of projected climate change on energy supply and demand in the Pacific Northwest and Washington State

Climate strongly affects energy supply and demand in the Pacific Northwest (PNW) and Washington State (WA). We evaluate potential effects of climate change on the seasonality and annual amount of PNW hydropower production, and on heating and cooling energy demand. Changes in hydropower production are estimated by linking simulated streamflow scenarios produced by a hydrology model to a simulation model of the Columbia River hydro system. Changes in energy demand are assessed using gridded estimates of heating degree days (HDD) and cooling degree days (CDD) which are then combined with population projections to create energy demand indices that respond both to climate, future population, and changes in residential air conditioning market penetration. We find that substantial changes in the amount and seasonality of energy supply and demand in the PNW are likely to occur over the next century in response to warming, precipitation changes, and population growth. By the 2040s hydropower production is projected to increase by 4.7–5.0% in winter, decrease by about 12.1–15.4% in summer, with annual reductions of 2.0–3.4%. Larger decreases of 17.1–20.8% in summer hydropower production are projected for the 2080s. Although the combined effects of population growth and warming are projected to increase heating energy demand overall (22–23% for the 2020s, 35–42% for the 2040s, and 56–74% for the 2080s), warming results in reduced per capita heating demand. Residential cooling energy demand (currently less than one percent of residential demand) increases rapidly (both overall and per capita) to 4.8–9.1% of the total demand by the 2080s due to increasing population, cooling degree days, and air conditioning penetration.