Individual Climate Models Research Articles

Weighting climate model projections using observational constraints.

Projected climate change integrates the net response to multiple climate feedbacks. Whereas existing long-term climate change projections are typically based on unweighted individual climate model simulations, as observed climate change intensifies it is increasingly becoming possible to constrain the net response to feedbacks and hence projected warming directly from observed climate change. One approach scales simulated future warming based on a fit to observations over the historical period, but this approach is only accurate for near-term projections and for scenarios of continuously increasing radiative forcing. For this reason, the recent Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) included such observationally constrained projections in its assessment of warming to 2035, but used raw model projections of longer term warming to 2100. Here a simple approach to weighting model projections based on an observational constraint is proposed which does not assume a linear relationship between past and future changes. This approach is used to weight model projections of warming in 2081–2100 relative to 1986–2005 under the Representative Concentration Pathway 4.5 forcing scenario, based on an observationally constrained estimate of the Transient Climate Response derived from a detection and attribution analysis. The resulting observationally constrained 5–95% warming range of 0.8–2.5 K is somewhat lower than the unweighted range of 1.1–2.6 K reported in the IPCC AR5.

Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information

It is highly anticipated that meteorological changes resulting from global climate change will affect the pattern of rice disease epidemics worldwide. Here, we evaluated the potential impacts of climate change on two representative rice diseases, leaf blast and sheath blight, in Korea. This study involves analyses of disease simulation using an epidemiological model, EPIRICE, which was validated for Korean rice paddy fields. The goal of our study was to assess likely changes in national disease probabilities using individual climate scenarios across different models and multi-model ensemble scenarios constructed by running 11 global climate models. In this way, the results from this study emphasize the uncertainties in climate change scenarios resulting from the variations in initial conditions as well as the structural differences in the global climate models. Observed and simulated epidemics for both diseases were compared using the area under the disease progress curve from EPIRICE model runs. Overall, the simulated incidence of epidemics for both diseases gradually decreased towards 2100 both from individual global climate models and multi-model ensembles. It was noted that while each individual model resulted in different magnitudes of impact, the multi-model ensemble gave the most reliable result that accounts for uncertainty compared to the individual models. In conclusion, we found that in modeling climate impacts on rice diseases, ensembles account for uncertainty better than individual climate models and can lead to better decision making.

The Modified Rainfall Anomaly Index (mRAI)—is this an alternative to the Standardised Precipitation Index (SPI) in evaluating future extreme precipitation characteristics?

Precipitation extremes affect various economic sectors and may result in substantial costs for societies. Future projections of such extreme occurrences are needed to successfully develop robust regional adaptation strategies. Model ensemble-based approaches provide a higher level of confidence since they compensate to some degree for the uncertainties of individual climate model projections. An ensemble of twelve regional climate projections from five regional climate models was used to evaluate the suitability of a modified version of the Rainfall Anomaly Index (mRAI) as an alternative to the Standardised Precipitation Index (SPI) in assessing future precipitation conditions. We compared frequency distributions and trends of the mRAI with the SPI for a test region that is climatologically representative of Central Eastern Europe. Both indices are highly correlated with each other at all tested timescales—both for stations and for regionally averaged data—with Pearson correlation coefficients >>0.9 and Spearman correlation coefficients >0.99. There are no significant differences in their frequency distributions, although the mRAI shows slightly higher frequencies in the classes of ‘moderately dry’ to ‘very dry’ conditions. The change signals revealed by SPI and mRAI are very similar for mean changes as well as for changes in the extremes. Considering the large bandwidth of change signals of individual regional climate projections, the mRAI provides sufficiently robust results for the evaluation of future precipitation anomaly trends. The notably more complex calculation of the SPI has no appreciable advantage for this application.

Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models

Electrical utility system operators must plan resources so that electricity supply matches demand throughout the day. As the proportion of wind‐generated electricity in the US grows, changes in daily wind patterns have the potential either to disrupt the utility or increase the value of wind to the system over time. Wind power projects are designed to last many years, so at this timescale, climate change may become an influential factor on wind patterns. We examine the potential effects of climate change on the average diurnal power production cycles at 12 locations in North America by analyzing averaged and individual output from nine high‐resolution regional climate models comprising historical (1971–1999) and future (2041–2069) periods. A semi‐parametric mixed model is fit using cubic B‐splines, and model diagnostics are checked. Then, a likelihood ratio test is applied to test for differences between the time periods in the seasonal daily averaged cycles, and agreement among the individual regional climate models is assessed. We investigate the significant changes by combining boxplots with a differencing approach and identify broad categories of changes in the amplitude, shape, and position of the average daily cycles. We then discuss the potential impact of these changes on wind power production. Copyright © 2015 John Wiley & Sons, Ltd.

A multi-model and multi-index evaluation of drought characteristics in the 21st century

Drought is a natural hazard that can have severe and long-lasting impacts on natural and human systems. Although increases in global greenhouse forcing are expected to change the characteristics and impacts of drought in the 21st century, there remains persistent uncertainty about how changes in temperature, precipitation and soil moisture will interact to shape the magnitude – and in some cases direction – of drought in different areas of the globe. Using data from 15 global climate models archived in the Coupled Model Intercomparison Project (CMIP5), we assess the likelihood of changes in the spatial extent, duration and number of occurrences of four drought indices: the Standardized Precipitation Index (SPI), the Standardized Runoff Index (SRI), the Standardized Precipitation–Evapotranspiration Index (SPEI) and the Supply–Demand Drought Index (SDDI). We compare these characteristics in two future periods (2010–2054 and 2055–2099) of the Representative Concentration Pathway 8.5 (RCP8.5). We find increases from the baseline period (1961–2005) in the spatial extent, duration and occurrence of “exceptional” drought in subtropical and tropical regions, with many regions showing an increase in both the occurrence and duration. There is strong agreement on the sign of these changes among the individual climate models, although some regions do exhibit substantial uncertainty in the magnitude of change. The changes in SPEI and SDDI characteristics are stronger than the changes in SPI and SRI due to the greater influence of temperature changes in the SPEI and SDDI indices. In particular, we see a robust permanent emergence of the spatial extent of SDDI from the baseline variability in West, East and Saharan Africa as early as 2020 and by 2080 in several other subtropical and tropical regions. The increasing likelihood of exceptional drought identified in our results suggests increasing risk of drought-related stresses for natural and human systems should greenhouse gas concentrations continue along their current trajectory.

Systematic uncertainty reduction strategies for developing streamflow forecasts utilizing multiple climate models and hydrologic models

Recent studies show that multimodel combinations improve hydroclimatic predictions by reducing model uncertainty. Given that climate forecasts are available from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to reduce the uncertainty in streamflow forecasts? To address this question, we consider three possible strategies: (1) reduce the input uncertainty first by combining climate models and then use the multimodel climate forecasts with multiple watershed models (MM-P), (2) ingest the individual climate forecasts (without multimodel combination) with various watershed models and then combine the streamflow predictions that arise from all possible combinations of climate and watershed models (MM-Q), (3) combine the streamflow forecasts obtained from multiple watershed models based on strategy (1) to develop a single streamflow prediction that reduces uncertainty in both climate forecasts and watershed models (MM-PQ). For this purpose, we consider synthetic schemes that generate streamflow and climate forecasts, for comparing the performance of three strategies with the true streamflow generated by a given hydrologic model. Results from the synthetic study show that reducing input uncertainty first (MM-P) by combining climate forecasts results in reduced error in predicting the true streamflow compared to the error of multimodel streamflow forecasts obtained by combining streamflow forecasts from all-possible combination of individual climate model with various hydrologic models (MM-Q). Since the true hydrologic model structure is unknown, it is desirable to consider MM-PQ as an alternate choice that reduces both input uncertainty and hydrologic model uncertainty. Application on two watersheds in NC also indicates that reducing the input uncertainty first is critical before reducing the hydrologic model uncertainty.

Changes in concurrent monthly precipitation and temperature extremes

While numerous studies have addressed changes in climate extremes, analyses of concurrence of climate extremes are scarce, and climate change effects on joint extremes are rarely considered. This study assesses the occurrence of joint (concurrent) monthly continental precipitation and temperature extremes in Climate Research Unit (CRU) and University of Delaware (UD) observations, and in 13 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate simulations. The joint occurrences of precipitation and temperature extremes simulated by CMIP5 climate models are compared with those derived from the CRU and UD observations for warm/wet, warm/dry, cold/wet, and cold/dry combinations of joint extremes. The number of occurrences of these four combinations during the second half of the 20th century (1951–2004) is assessed on a common global grid. CRU and UD observations show substantial increases in the occurrence of joint warm/dry and warm/wet combinations for the period 1978–2004 relative to 1951–1977. The results show that with respect to the sign of change in the concurrent extremes, the CMIP5 climate model simulations are in reasonable overall agreement with observations. However, the results reveal notable discrepancies between regional patterns and the magnitude of change in individual climate model simulations relative to the observations of precipitation and temperature.

Use of Representative Climate Futures in impact and adaptation assessment

A key challenge for climate projection science is to serve the rapidly growing needs of impact and adaptation assessments (hereafter risk assessments) in an environment where there are substantial differences in the regional projections of climate models, an expanding number of potentially relevant climate model results, and a desire amongst many users to limit the number of future climate scenarios in their assessments. While it may be attractive to select a small number of climate models based on their ability to replicate current climate, there is no robust method for doing this. We outline and illustrate a method that addresses this challenge in a different way. The range of plausible future climates simulated by climate models is classified into a small set of Representative Climate Futures (RCFs) and the relative likelihood of these estimated. For each region, the RCFs are then used as a framework in which to classify more detailed information, such as available climate model and downscaled data sets. Researchers wishing to apply the RCFs in risk assessments can then choose to use a subset of RCFs, such as the “most likely”, “high risk” and “least change” cases for their impact system. Preparation and analysis of future climate data sets can therefore be confined to those models whose simulations best represent the selected RCFs. This significantly reduces the number of models involved, and potentially the effort required to undertake the risk assessment. Consistently applied within a region, RCFs, rather than individual climate models, can become the boundary objects which anchor discussion between the climate science and risk assessment communities, simplifying communication. Since the RCF descriptions need not change as new climate model results emerge, they can also provide a stable framework for assimilating risk assessments undertaken at different times with different sets of climate models. Systematic application of this approach requires various challenges to be addressed, such as robustly classifying future regional climates into a small set and estimating likelihoods.

Time of emergence of climate signals

The time at which the signal of climate change emerges from the noise of natural climate variability (Time of Emergence, ToE) is a key variable for climate predictions and risk assessments. Here we present a methodology for estimating ToE for individual climate models, and use it to make maps of ToE for surface air temperature (SAT) based on the CMIP3 global climate models. Consistent with previous studies we show that the median ToE occurs several decades sooner in low latitudes, particularly in boreal summer, than in mid‐latitudes. We also show that the median ToE in the Arctic occurs sooner in boreal winter than in boreal summer. A key new aspect of our study is that we quantify the uncertainty in ToE that arises not only from inter‐model differences in the magnitude of the climate change signal, but also from large differences in the simulation of natural climate variability. The uncertainty in ToE is at least 30 years in the regions examined, and as much as 60 years in some regions. Alternative emissions scenarios lead to changes in both the median ToE (by a decade or more) and its uncertainty. The SRES B1 scenario is associated with a very large uncertainty in ToE in some regions. Our findings have important implications for climate modelling and climate policy which we discuss.

Maximum-entropy weighting of multiple earth climate models

A maximum entropy-based framework is presented for the synthesis of projections from multiple Earth climate models. This identifies the most representative (most probable) model from a set of climate models -- as defined by specified constraints -- eliminating the need to calculate the entire set. Two approaches are developed, based on individual climate models or ensembles of models, subject to a single cost (energy) constraint or competing cost-benefit constraints. A finite-time limit on the minimum cost of modifying a model synthesis framework, at finite rates of change, is also reported.

Assessing observed and modelled spatial distributions of ice water path using satellite data

Abstract. The climate models used in the IPCC AR4 show large differences in monthly mean ice water path (IWP). The most valuable source of information that can be used to potentially constrain the models is global satellite data. The satellite datasets also have large differences. The retrieved IWP depends on the technique used, as retrievals based on different techniques are sensitive to different parts of the cloud column. Building on the foundation of Waliser et al. (2009), this article provides a more comprehensive comparison between satellite datasets. IWP data from the CloudSat cloud profiling radar provide the most advanced dataset on clouds. For all its unmistakable value, CloudSat data are too short and too sparse to assess climatic distributions of IWP, hence the need to also use longer datasets. We evaluate satellite datasets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, in order to determine the differences and relate them to the sensitivity of the instrument used in the retrievals. This information is also used to evaluate the climate models, to the extent that is possible. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the better of the two. Additionally PATMOS-x and ISCCP, which have a temporal range long enough to capture the inter-annual variability of IWP, are used in conjunction with CloudSat IWP (after removing profiles that contain precipitation) to assess the IWP variability and mean of the climate models. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is probably the GCM from IPCC AR4 closest to satellite observations.

Watershed Modeling to Assessing Impacts of Potential Climate Change on Water Supply Availability

Climate change could have impacts on hydrologic systems threatening, availability of water supply resources. In Illinois, regional water supply planning efforts are attempting to better understand potential impacts on low flow and surface water availability through analysis of hydrologic sensitivity to a range of climate scenarios. This paper explores the development, calibration and validation of Fox River watershed model using the soil and water assessment tool (SWAT) and the model’s application to assess impacts of potential climate change. The watershed model is calibrated and validated using daily flow records at three gauging stations. Automatic model calibration followed by manual refinement of parameter values was performed. Calibration results were generally good for monthly and annual time step but only satisfactory for daily simulations. Based on simulations of global climate models produced for IPCC fourth assessment report, climate scenarios were prepared by the Illinois State Water Survey for water supply planning initiatives in north-east and east-central Illinois. These scenarios showed ranges of temperature change between 0°C to +3.3°C and annual precipitation changes between −127 to +127 mm in the next 50 years, excluding the 5% extreme ends of those climate model simulations considered. Changes in climate were reflected using adjustments to the historical record, instead of using direct outputs from individual climate models. The watershed model was used to assess the impact of potential climate change. Application results indicate that annual precipitation change of 127 mm on average increases annual water yield and 7-day low flows by 28% and 19%, respectively. In contrast, a temperature change of +3.3°C results in average reductions of annual water yield by 13% and 7-day low flows by 10%. Seasonal effects were investigated through evaluation of changes in average monthly flows. Increasing precipitation resulted in significant changes in streamflows in late summer and fall months where as increasing temperature greatly affects winter flows due to snowmelt. The key implication is that climate change-induced variability of streamflows could have major impacts on water supply availability in the Fox River watershed and in particular, increased periods of drought could result in deficit of supplies during seasons of peak water use. It must be noted that this analysis does not examine the potential impacts of population growth and water use on water supply availability, which are also expected to have substantial influences in the region.

Impact of climate change on Pacific Northwest hydropower

The Pacific Northwest (PNW) hydropower resource, central to the region’s electricity supply, is vulnerable to the impacts of climate change. The Northwest Power and Conservation Council (NWPCC), an interstate compact agency, has conducted long term planning for the PNW electricity supply for its 2005 Power Plan. In formulating its power portfolio recommendation, the NWPCC explored uncertainty in variables that affect the availability and cost of electricity over the next 20 years. The NWPCC conducted an initial assessment of potential impacts of climate change on the hydropower system, but these results are not incorporated in the risk model upon which the 2005 Plan recommendations are based. To assist in bringing climate information into the planning process, we present an assessment of uncertainty in future PNW hydropower generation potential based on a comprehensive set of climate models and greenhouse gas emissions pathways. We find that the prognosis for PNW hydropower supply under climate change is worse than anticipated by the NWPCC’s assessment. Differences between the predictions of individual climate models are found to contribute more to overall uncertainty than do divergent emissions pathways. Uncertainty in predictions of precipitation change appears to be more important with respect to impact on PNW hydropower than uncertainty in predictions of temperature change. We also find that a simple regression model captures nearly all of the response of a sequence of complex numerical models to large scale changes in climate. This result offers the possibility of streamlining both top-down impact assessment and bottom-up adaptation planning for PNW water and energy resources.

Transferability Intercomparison: An Opportunity for New Insight on the Global Water Cycle and Energy Budget

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.