Evaluation of regional climate simulations for air quality modelling purposes

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional “climate modeling” source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.     

The role of the simulation setup in a long-term high-resolution climate change projection for the southern African region

High-resolution regional climate change simulations have proven to offer an added value compared to available global climate model simulations. However, over many regions of the globe, long-term high-resolution climate change projections are rather sparse. We present a transient high-resolution climate change projection with the regional climate model with the regional climate model REMO over the southern African region, following the SRES A1B emission scenario. The simulation was conducted at 18?km grid spacing for the period from 1960 to 2100, making it to the longest available climate change projection at such a high resolution for the region. In the first part of the study, we focus on the impact of the model setup on the simulated rainfall over the southern African region. In the standard setup, we used the output of the global climate model ECHAM5/MPIOM directly to force REMO. This setup led to a very strong wet bias over the region. Changing it to the double-nesting setup significantly reduced this bias, but a substantial wet bias still persists. The remaining bias could partly be attributed to a warm bias in the SST forcing over the southern Atlantic Ocean. Thus, we applied an SST correction based on the anomaly approach to the data, which led to a further improvement of the rainfall simulation. As the SST bias in the southern Atlantic is a common feature of all global climate models assessed by the IPCC, we recommend the chosen model setup, including the SST correction, as general procedure for dynamical downscaling studies over the southern African region. In the second part, we present the projected spatial and temporal changes of temperature and precipitation, including several rainfall characteristics, over the southern African region. Herby we compare the projections of the high-resolution REMO simulation to those of the forcing regional and global models. We generally find that for temperature the magnitude of the projected changes of the regional model only slightly differs from the GCM projection; however, the spatial patterns are much better resolved in the RCM projections. For precipitation, REMO shows a more intense drying toward the end of the twenty-first century than it is simulated by the global model. This can have a major influence when investigating the impacts of future climate change on a regional or even local scale. In combination with the improved spatial patterns, the application of high-resolution climate change information could therefore improve the results of such applications.     

On interpreting hydrological change from regional climate models

Although representation of hydrology is included in all regional climate models (RCMs), the utility of hydrological results from RCMs varies considerably from model to model. Studies to evaluate and compare the hydrological components of a suite of RCMs and their use in assessing hydrological impacts from future climate change were carried out over Europe. This included using different methods to transfer RCM runoff directly to river discharge and coupling different RCMs to offline hydrological models using different methods to transfer the climate change signal between models. The work focused on drainage areas to the Baltic Basin, the Bothnian Bay Basin and the Rhine Basin. A total of 20 anthropogenic climate change scenario simulations from 11 different RCMs were used. One conclusion is that choice of GCM (global climate model) has a larger impact on projected hydrological change than either selection of emissions scenario or RCM used for downscaling.     

Sources of knowledge and ignorance in climate research

Ignorance is an inevitable component of climate change research, and yet it has not been specifically catered for in standard uncertainty guidance documents for climate assessments. Reports of ignorance in understanding require context to explain how such ignorance does and does not affect understanding more generally. The focus of this article is on dynamical sources of ignorance in regional climate change projections. A key source of ignorance in the projections is the resolution-limited treatment of dynamical instabilities in the ocean component of coupled climate models. A consequence of this limitation is that it is very difficult to quantify uncertainty in regional projections of climate variables that depend critically upon the details of the atmospheric flow. The standard methods for quantifying or reducing uncertainty in regional projections are predicated on the models capturing and representing the key dynamical instabilities, which is doubtful for present coupled models. This limitation does not apply to all regional projections, nor does it apply to the fundamental findings of greenhouse climate change. Much of what is known is not highly flow-dependent and follows from well grounded radiative physics and thermodynamic principles. The growing field of applications of regional climate projections would benefit from a more critical appraisal of ignorance in these projections.     

Assessing climate change impacts on hydrology from an ensemble of regional climate models,model scales and linking methods – a case study on the Lule River basin

This paper investigates how using different regional climate model (RCM) simulations affects climate change impacts on hydrology in northern Europe using an offline hydrological model. Climate change scenarios from an ensemble of seven RCMs, two global climate models (GCMs), two global emissions scenarios and two RCMs of varying resolution were used. A total of 15 climate change simulations were included in studies on the Lule River basin in Northern Sweden. Two different approaches to transfer climate change from the RCMs to hydrological models were tested. A rudimentary estimate of change in hydropower potential on the Lule River due to climate change was also made. The results indicate an overall increase in river flow, earlier spring peak flows and an increase in hydropower potential. The two approaches for transferring the signal of climate change to the hydrological impacts model gave similar mean results, but considerably different seasonal dynamics, a result that is highly relevant for other types of climate change impacts studies.     

Interdecadal variability of regional climate change: implications for the development of regional climate change scenarios

Summary Illustrative examples are discussed of the interdecadal variability features of the regional climate change signal in 5 AOGCM transient simulations. It is shown that the regional precipitation change signal is characterized by large variability at decadal to multidecadal scales, with the structure of the variability varying markedly across regions. Conversely, the regional temperature change signal shows low interdecadal variability. Results are compared across scenarios, models and different realizations with the same model. Our analysis indicates that, at the decadal scale, linear scaling of the regional climate change signal by the global temperature change works relatively well for temperature but less so for precipitation. The nonlinear fraction of the climate change signal tends to decrease with the magnitude of the signal. The implications of interdecadal variability for the generation of regional climate change scenarios are discussed, in particular concerning the use of multi-experiment ensembles to produce such scenarios.     

Present-day and future precipitation in the Baltic Sea region as simulated in a suite of regional climate models

Here we investigate simulated changes in the precipitation climate over the Baltic Sea and surrounding land areas for the period 2071–2100 as compared to 1961–1990. We analyze precipitation in 10 regional climate models taking part in the European PRUDENCE project. Forced by the same global driving climate model, the mean of the regional climate model simulations captures the observed climatological precipitation over the Baltic Sea runoff land area to within 15% in each month, while single regional models have errors up to 25%. In the future climate, the precipitation is projected to increase in the Baltic Sea area, especially during winter. During summer increased precipitation in the north is contrasted with a decrease in the south of this region. Over the Baltic Sea itself the future change in the seasonal cycle of precipitation is markedly different in the regional climate model simulations. We show that the sea surface temperatures have a profound impact on the simulated hydrological cycle over the Baltic Sea. The driving global climate model used in the common experiment projects a very strong regional increase in summertime sea surface temperature, leading to a significant increase in precipitation. In addition to the common experiment some regional models have been forced by either a different set of Baltic Sea surface temperatures, lateral boundary conditions from another global climate model, a different emission scenario, or different initial conditions. We make use of the large number of experiments in the PRUDENCE project, providing an ensemble consisting of more than 25 realizations of climate change, to illustrate sources of uncertainties in climate change projections.     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Regional cooperation in widening energy access and also mitigating climate change: Current programs and future potential

Access to energy can significantly contribute to the development of the living standards of the energy poor. Also if the provision of access to energy is sustainable, i.e. via renewable energy sources, there is an added benefit of contributing to mitigation of climate change. Currently, the percentage of population with access to energy varies significantly between countries and across regions. This is due to the nature of national socio-economic situations and energy resource availability in differing settings. This article addresses issues and hindrances to energy access in regional contexts and also examines, in particular, the prospects of how regional cooperation initiatives linked with climate change mitigation objectives could assist in widening energy access. Existing relevant regional cooperation initiatives that may be upscaled or used as models to widen access to modern energy services are evaluated. Findings are that regional cooperation initiatives linked with climate change mitigation can potentially facilitate widening energy access. However, in order to realise such potential, synergies from regional cooperation that are indirectly linked to energy and wider climate change mitigation programs should be harnessed. Recommendations are made for development of sustainable energy programs in energy deprived regions that will also mitigate climate change impacts.     

Internal variability of regional climate models

 Two regional climate models have been applied to the task of generating an ensemble of realizations of the year 1982 with observed boundary conditions in areas covering parts of the Mediterranean countries. These realizations were generated by applying boundary conditions from the ECMWF ERA reanalysis project consecutively, carrying over the soil variables from the regional models from one iteration to the next. Monthly mean fields for six iterations of each model have been used as statistical ensembles in order to investigate the internal variability of the regional model dynamics. This internal variability is a necessary consequence of the non-linear physical feedback mechanisms of the RCM being active. A small value of internal variability will give better statistics for climate sensitivity signals, but will make these results less credible. The internal variability is important for the quantitative assessment of a climate sensitivity signal. With the present choice of models and integration domains the internal variabilities of surface fields and precipitation do reach levels that are less than, but in summer of comparable order of magnitude to, corresponding atmospheric variabilities of an atmospheric general circulation model. Received: 26 October 1999 / Accepted: 18 December 2000     

Global-scale climate impact functions: the relationship between climate forcing and impact

Although there is a strong policy interest in the impacts of climate change corresponding to different degrees of climate change, there is so far little consistent empirical evidence of the relationship between climate forcing and impact. This is because the vast majority of impact assessments use emissions-based scenarios with associated socio-economic assumptions, and it is not feasible to infer impacts at other temperature changes by interpolation. This paper presents an assessment of the global-scale impacts of climate change in 2050 corresponding to defined increases in global mean temperature, using spatially-explicit impacts models representing impacts in the water resources, river flooding, coastal, agriculture, ecosystem and built environment sectors. Pattern-scaling is used to construct climate scenarios associated with specific changes in global mean surface temperature, and a relationship between temperature and sea level used to construct sea level rise scenarios. Climate scenarios are constructed from 21 climate models to give an indication of the uncertainty between forcing and response. The analysis shows that there is considerable uncertainty in the impacts associated with a given increase in global mean temperature, due largely to uncertainty in the projected regional change in precipitation. This has important policy implications. There is evidence for some sectors of a non-linear relationship between global mean temperature change and impact, due to the changing relative importance of temperature and precipitation change. In the socio-economic sectors considered here, the relationships are reasonably consistent between socio-economic scenarios if impacts are expressed in proportional terms, but there can be large differences in absolute terms. There are a number of caveats with the approach, including the use of pattern-scaling to construct scenarios, the use of one impacts model per sector, and the sensitivity of the shape of the relationships between forcing and response to the definition of the impact indicator.     

The bias of integrated assessment models that ignore climate catastrophes

Climate scientists currently predict there is a small but real possibility that climate change will lead to civilization threatening catastrophic events. Martin Weitzman has used this evidence along with his controversial “Dismal Theorem” to argue that integrated assessment models of climate change cannot be used to determine an optimal price for carbon dioxide. In this paper, I provide additional support for Weitzman’s conclusions by running numerical simulations to estimate risk premiums toward climate catastrophes. Compared to the assumptions found in most integrated assessment models, I incorporate into the model a more realistic range of uncertainty for both climate catastrophes and societal risk aversion. The resulting range of risk premiums indicates that the conclusions drawn from integrated assessment models that do not incorporate the potential for climate catastrophes are too imprecise to support any particular policy recommendation. The analysis of this paper is more straightforward and less technical than Weitzman’s, and therefore the conclusions should be accessible to a wider audience.     

A summary of the PRUDENCE model projections of changes in European climate by the end of this century

An overview of the PRUDENCE fine resolution climate model experiments for Europe is presented in terms of their climate change signals, in particular 2-meter temperature and precipitation. A comparison is made with regard to the seasonal variation in climate change response of the different models participating in the project. In particular, it will be possible to check how representative a particular PRUDENCE regional experiment is of the overall set in terms of seasonal values of temperature and precipitation. This is of relevance for such further studies and impact models that for practical reasons cannot use all the PRUDENCE regional experiments. This paper also provides some guidelines for how to select subsets of the PRUDENCE regional experiments according to such main sources of uncertainty in regional climate simulations as the choice of the emission scenario and of the driving global climate model.     

The impact of perturbations to ocean-model parameters on climate and climate change in a coupled model

Assessments of the impacts of uncertainties in parameters on mean climate and climate change in complex climate models have, to date, largely focussed on perturbations to parameters in the atmosphere component of the model. Here we expand on a previously published study which found the global impacts of perturbed ocean parameters on the rate of transient climate change to be small compared to perturbed atmosphere parameters. By separating the climate-change-induced ocean vertical heat transport in each perturbed member into components associated with the resolved flow and each parameterisation scheme, we show that variations in global mean heat uptake in different perturbed versions are an order of magnitude smaller than the average heat uptake. The lack of impact of the perturbations is attributed to (1) the relatively small impact of the perturbation on the direct vertical heat transport associated with the perturbed process and (2) a compensation between those direct changes and indirect changes in heat transport from other processes. Interactions between processes and changes appear to combine in complex ways to limit ensemble spread and uncertainty in the rate of warming. We also investigate regional impacts of the perturbations that may be important for climate change predictions. We find variations across the ensemble that are significant when measured against natural variability. In terms of the experimental set-up used here (models without flux adjustments) we conclude that perturbed physics ensembles with ocean parameter perturbations are an important component of any probabilistic estimate of future climate change, despite the low spread in global mean quantities. Hence, careful consideration should be given to assessing uncertainty in ocean processes in future probabilistic assessments of regional climate change.     

从全球到区域气候变化

尽管气候变化是全球性的现象,但其表现和结果随区域不同而不同,因此区域气候信息对于气候变化的作用和风险评估很重要。基于此,IPCC第六次评估报告（AR6）第一工作组（WGI）报告第十章对如何从全球链接到区域气候变化方面进行了评估。区域气候变化是对自然强迫和人类活动的区域响应、对大尺度气候系统内部变率的响应和区域气候本身反馈过程的相互作用结果。因此,本章重点关注如何从多套观测资料,不同模式的集合,物理过程的理解、专家判断和本地信息等多元信息中有效提炼出区域信息的方法。通过提炼方法指出人类活动是许多次大陆尺度上1950年代以来区域平均温度变化的主要驱动力,但参考时段和阈值的选择对人类活动信号是否出现和出现的早晚有影响。人类活动对一些区域的多年代际降水变化有一定贡献,但其不确定性相对全球平均而言更大。气候系统内部变率可以在很大程度上延迟和阻碍人类活动信号在区域气候变化中的出现。区域气候变化的评估给决策者提供了更多有用的信息,增加了评估报告的适用性。     

Shoreline evolution under climate change wave scenarios

This paper investigates changes in shoreline evolution caused by changes in wave climate. In particular, a number of nearshore wave climate scenarios corresponding to a ??present?? (1961?C1990) and a future time-slice (2071?C2100) are used to drive a beach evolution model to determine monthly and seasonal statistics. To limit the number of variables, an idealised shoreline segment is adopted. The nearshore wave climate scenarios are generated from wind climate scenarios through point wave hindcast and inshore transformation. The original wind forcing comes from regional climate change model experiments of different resolutions and/or driving global climate models, representing different greenhouse-gas emission scenarios. It corresponds to a location offshore the south central coast of England. Hypothesis tests are applied to map the degree of evidence of future change in wave and shoreline statistics relative to the present. Differential statistics resulting from different global climate models and future emission scenarios are also investigated. Further, simple, fast, and straightforward methods that are capable of accommodating a great number of climate change scenarios with limited data reduction requirements are proposed to tackle the problem under consideration. The results of this study show that there are statistically significant changes in nearshore wave climate conditions and beach alignment between current and future climate scenarios. Changes are most notable during late summer for the medium-high future emission scenario and late winter for the medium-low. Despite frequent disagreement between global climate change models on the statistical significance of a change, all experiments agreed in future seasonal trends. Finally, a point of importance for coastal management, material shoreline changes are generally linked to significant changes in future wave direction rather than wave height.     

Regionally optimized forest management under changing climate

The purpose of this study was to optimize forest management for a forest region (the total area of forest and scrub land 1.54 mill. ha) under changing climate by using the large-scale forestry scenario model MELA and sample plot data from the geo-referenced National Forest Inventory (NFI). The MELA model is based on integrated simulation and optimisation; in the simulation it utilises empirical tree-level models into which the impacts of climate change were introduced by transfer variables derived by using the physiological model FinnFor. Six scenarios with differences in climate and forest management were defined. In simulations, the accelerating tree growth caused by climate change resulted in an increase in maximum sustainable removal of trees at regional level. Changes in regionally optimized forest management were also detected during the analysis period of 30 years; the proportion of thinnings increased because the stands fulfilled the thinning requirements earlier than in the current climate. This study was the first attempt to solve endogenously maximum sustainable timber production and corresponding forest management at the regional level under different climate scenarios. When implemented in the MELA system, which is widely used in Finnish forestry, the transfer variables offer means of disseminating the results from physiological studies to planning of adjustment and mitigation measures under changing climate.     

The local impacts of climate change in the Ferlo, Western Sahel

Recent increases in the accuracy of climate models have enhanced the possibilities for analyzing the impacts of climate change on society. This paper explores how the local, economic impacts of climate change can be modeled for a specific eco-region, the Western Sahel. The people in the Sahel are highly dependent on their natural resource base, and these resources are highly vulnerable to climate change, in particular to changes in rainfall. Climate models project substantial changes in rainfall in the Sahel in the coming 50 years, with most models predicting a reduction in rainfall. To connect climate change to changes in ecosystem productivity and local income, we construct an ecological–economic model that incorporates rangeland dynamics, grazing and livestock prices. The model shows that decreased rainfall in the Sahel will considerably reduce local incomes, in particular if combined with increases in rainfall variability. Adaptation to these climate change projections is possible if reductions in rainfall are followed by destocking to reach efficient grazing levels. However, while such a strategy is optimal from the perspective of society, the stocking rate is determined by individual pastoralists that face few incentives to destock.     

Trade-offs associated with different modeling approaches for assessment of fish and shellfish responses to climate change

Considerable progress has been made in integrating carbon, nutrient, phytoplankton and zooplankton dynamics into global-scale physical climate models. Scientists are exploring ways to extend the resolution of the biosphere within these Earth system models (ESMs) to include impacts on global distribution and abundance of commercially exploited fish and shellfish. This paper compares different methods for modeling fish and shellfish responses to climate change on global and regional scales. Several different modeling approaches are considered including: direct applications of ESM’s, use of ESM output for estimation of shifts in bioclimatic windows, using ESM outputs to force single- and multi-species stock projection models, and using ESM and physical climate model outputs to force regional bio-physical models of varying complexity and mechanistic resolution. We evaluate the utility of each of these modeling approaches in addressing nine key questions relevant to climate change impacts on living marine resources. No single modeling approach was capable of fully addressing each question. A blend of highly mechanistic and less computationally intensive methods is recommended to gain mechanistic insights and to identify model uncertainties.     

Future climate resources for tourism in Europe based on the daily Tourism Climatic Index

Climate is an important resource for many types of tourism. One of several metrics for the suitability of climate for sightseeing is Mieczkowski’s “Tourism Climatic Index” (TCI), which summarizes and combines seven climate variables. By means of the TCI, we analyse the present climate resources for tourism in Europe and projected changes under future climate change. We use daily data from five regional climate models and compare the reference period 1961–1990 to the A2 scenario in 2071–2100. A comparison of the TCI based on reanalysis data and model simulations for the reference period shows that current regional climate models capture the important climatic patterns. Currently, climate resources are best in Southern Europe and deteriorate with increasing latitude and altitude. With climate change the latitudinal band of favourable climate is projected to shift northward improving climate resources in Northern and Central Europe in most seasons. Southern Europe’s suitability for sightseeing tourism drops strikingly in the summer holiday months but is partially compensated by considerable improvements between October and April.