Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

Observation-based blended projections from ensembles of regional climate models

We consider the problem of projecting future climate from ensembles of regional climate model (RCM) simulations using results from the North American Regional Climate Change Assessment Program (NARCCAP). To this end, we develop a hierarchical Bayesian space-time model that quantifies the discrepancies between different members of an ensemble of RCMs corresponding to present day conditions, and observational records. Discrepancies are then propagated into the future to obtain high resolution blended projections of 21st century climate. In addition to blended projections, the proposed method provides location-dependent comparisons between the different simulations by estimating the different modes of spatial variability, and using the climate model-specific coefficients of the spatial factors for comparisons. The approach has the flexibility to provide projections at customizable scales of potential interest to stakeholders while accounting for the uncertainties associated with projections at these scales based on a comprehensive statistical framework. We demonstrate the methodology with simulations from the Weather Research & Forecasting regional model (WRF) using three different boundary conditions. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the Special Report on Emissions Scenarios (SRES) A2 emissions scenario, covering 2041 to 2070. We investigate and project yearly mean summer and winter temperatures for a domain in the South West of the United States.     

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.     

Climate change signal and uncertainty in projections of ocean wave heights

In this study, projections of seasonal means and extremes of ocean wave heights were made using projections of sea level pressure fields conducted with three global climate models for three forcing-scenarios. For each forcing-scenario, the three climate models’ projections were combined to estimate the multi-model mean projection of climate change. The relative importance of the variability in the projected wave heights that is due to the forcing prescribed in a forcing-scenario was assessed on the basis of ensemble simulations conducted with the Canadian coupled climate model CGCM2. The uncertainties in the projections of wave heights that are due to differences among the climate models and/or among the forcing-scenarios were characterized. The results show that the multi-model mean projection of climate change has patterns similar to those derived from using the CGCM2 projections alone, but the magnitudes of changes are generally smaller in the boreal oceans but larger in the region nearby the Antarctic coastal zone. The forcing-induced variance (as simulated by CGCM2) was identified to be of substantial magnitude in some areas in all seasons. The uncertainty due to differences among the forcing-scenarios is much smaller than that due to differences among the climate models, although it was identified to be statistically significant in most areas of the oceans (this indicates that different forcing conditions do make notable differences in the wave height climate change projection). The sum of the model and forcing-scenario uncertainties is smaller in the JFM and AMJ seasons than in other seasons, and it is generally small in the mid-high latitudes and large in the tropics. In particular, some areas in the northern oceans were projected to have large changes by all the three climate models.     

River discharge to the Baltic Sea in a future climate

This study reports on new projections of discharge to the Baltic Sea given possible realisations of future climate and uncertainties regarding these projections. A high-resolution, pan-Baltic application of the Hydrological Predictions for the Environment (HYPE) model was used to make transient simulations of discharge to the Baltic Sea for a mini-ensemble of climate projections representing two high emissions scenarios. The biases in precipitation and temperature adherent to climate models were adjusted using a Distribution Based Scaling (DBS) approach. As well as the climate projection uncertainty, this study considers uncertainties in the bias-correction and hydrological modelling. While the results indicate that the cumulative discharge to the Baltic Sea for 2071 to 2100, as compared to 1971 to 2000, is likely to increase, the uncertainties quantified from the hydrological model and the bias-correction method show that even with a state-of-the-art methodology, the combined uncertainties from the climate model, bias-correction and impact model make it difficult to draw conclusions about the magnitude of change. It is therefore urged that as well as climate model and scenario uncertainty, the uncertainties in the bias-correction methodology and the impact model are also taken into account when conducting climate change impact studies.     

Sensitivity of climate change projections to uncertainties in the estimates of observed changes in deep-ocean heat content

The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed twentieth century temperature changes. The impact on climate change projections of using the smallest and largest estimates of twentieth century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference (“business as usual”) scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by twentieth century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4’s multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However, the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the twentieth century changes in surface and upper air temperatures, combined with the expert prior for climate sensitivity.     

Assessment of Climate Change Impacts on the Economic Development of the Russian Arctic in the 21st Century

The problem of increasing the informativeness of climate projections in the Russian Arctic in order to meet the current economy needs is considered. The detailed estimates are presented of changes for the most important specialized indicators of the thermal and moisture regimes which characterize climatic impacts on the economic development of the Russian Arctic in the 21st century. The calculations are based on the data of numerical experiments with the regional climate model which were conducted for the Arctic region in the framework of the international CORDEX project. The high resolution of the model (50 km) and the consideration of mesoscale factors helped to detect significant spatial differences in the estimates of changes in the analyzed parameters which should be taken into account when adapting to climate change at the regional level.     

Social,environmental and health vulnerability to climate change in the Brazilian Northeastern Region

A regional vulnerability study in relation to the projected patterns of climate change (A2 and B2 scenarios) was developed for the Brazilian Northeastern region. An aggregated Vulnerability Index was constructed for each of the nine States of the region, based on the following information: population projections; climate-induced migration scenarios; disease trends; desertification rates; economic projections (GDP and employment) and projections for health care costs. The results obtained shall subsidize public policies for the protection of the human population from the projected impacts of regional changes in climate patterns.     

Impacts of bias correction of lateral boundary conditions on regional climate projections in West Africa

Biases existing in the lateral boundary conditions (LBCs) influence climate simulations in regional climate models (RCMs). Correcting the biases in global climate model (GCM)-produced LBCs before running RCMs was proposed in previous studies as a possible way to reduce the GCM-related model dependence of future climate projections using RCMs. In this study the ICTP Regional Climate Model Version 4 (RegCM4) is used to investigate the impact of LBC bias correction on projected future changes of regional climate in West Africa. To accomplish this, two types of present versus future simulations are conducted using RegCM4: a control type where both the present and future LBCs are derived directly from the GCM output (as is done in most regional climate downscaling studies); an experiment type where the present-day LBCs are from reanalysis data and future LBCs are derived by combining the reanalysis data and the GCM-projected LBC changes. For each type of simulations, three different sets of LBCs are experimented on: 6-hourly synoptic forcing directly from the reanalysis or GCM, 6-hourly data interpolated from monthly climatology (without diurnal cycle), and 6-hourly data interpolated from the month-specific climatology of diurnal cycles. It is found that the simulations using different LBCs produce similar present-day summer rainfall patterns, but the predicted future changes differ significantly depending on how the LBC bias correction is treated. Specifically, both the bias correction applied at the synoptic scale and the bias correction applied to the monthly interpolated LBCs without diurnal cycle produce a spurious drying signal caused by physical inconsistency in the corrected future LBCs. Interpolated monthly LBCs with diurnal cycle alleviate the problem to a large extent. These results suggest that using bias-corrected LBCs to drive regional climate models may not guarantee reliable future projections although reasonable present climate can be simulated. Physical inconsistencies may be contained in the bias-corrected LBCs, increasing the uncertainties of RCM-produced future projections.     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

IPCC第六次气候变化评估中的气候约束预估方法

得益于第五次评估报告（AR5）以来约束预估研究的迅速发展,观测约束成为政府间气候变化专门委员会（IPCC）第一工作组（WGI）第六次评估报告（AR6）提升对未来预估约束的证据链中的重要一环。IPCC第一工作组第六次评估报告首次利用包括根据历史模拟温度升高幅度得到的观测约束、多模式预估以及第六次评估报告中更新的气候敏感度在内的多条证据链来约束全球地表温度未来变化的预估,减小了多模式预估的不确定性。文中回顾并介绍了IPCC第一工作组第六次评估报告中涉及的几种主要观测约束方法（多模式加权方法、基于归因结论的约束方法（ASK方法）、萌现约束方法）及其应用情况。在IPCC第一工作组第六次评估报告以及很多针对不同区域不同变量的预估研究中,观测约束方法均显示出了订正模式误差、改善模式预估的潜力。相比而言,目前中国在观测约束预估方面的研究还不多,亟待加强观测约束方法研究以及在中国区域气候变化预估中的应用,为中国应对气候变化的政策制定和适应规划提供更丰富、不确定性更小的未来气候信息。      

A Demonstration of the Uncertainty in Projections of UK Climate Change Resulting from Regional Model Formulation

Regional climate models (RCMs) are now commonly used to downscale climate change projections provided by global coupled models to resolutions that can be utilised at national and finer scales. Although this extra tier of complexity adds significant value, it inevitably contributes a further source of uncertainty, due to the regional modelling uncertainties involved. Here, an initial attempt is made to estimate the uncertainty that arises from typical variations in RCM formulation, focussing on changes in UK surface air temperature (SAT) and precipitation projected for the late twenty-first century. Data are provided by a relatively large suite of RCM and global model integrations with widely varying formulations. It is found that uncertainty in the formulation of the RCM has a relatively small, but non-negligible, impact on the range of possible outcomes of future UK seasonal mean climate. This uncertainty is largest in the summer season. It is also similar in magnitude to that of large-scale internal variations of the coupled climate system, and for SAT, it is less than the uncertainty due to the emissions scenario, whereas for precipitation it is probably larger. The largest source of uncertainty, for both variables and in all seasons, is the formulation of the global coupled model. The scale-dependency of uncertainty due to RCM formulation is also explored by considering its impact on projections of the difference in climate change between the north and south of the UK. Finally, the implications for the reliability of UK seasonal mean climate change projections are discussed.     

A relation between extreme daily precipitation and extreme short term precipitation

The Royal Netherlands Meteorological Institute (KNMI) has published the KNMI’06 climate scenarios in 2006. These scenarios give the possible states of the climate in The Netherlands for the next century. Projections of changes in precipitation were made for a time scale of 1 day. The urban drainage sector is, however, more interested in projections on shorter time scales. Specifically, time scales of 1 h or less. The aim of this research is to provide projections of precipitation at these shorter time scales based on the available daily scenarios. This involves an analysis of climate variables and their relations to precipitation at different time scales. On the basis of this analysis, one can determine a numeric factor to translate daily projections into shorter time scale projections.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.     

An assessment of potential change in wildfire activity in the Russian boreal forest zone induced by climate warming during the twenty-first century

The problem of forest fires is very important for Russia. In this paper we consider this problem in the connection with the projection of significant climate change. An approach to determine the magnitude of change in wildfire risk in Russia under the influence of climate warming is discussed. Observations for the European part of Russia and for Siberia have been used in this analysis. A statistical correlation between drought indices calculated by use of monthly sums of temperature and precipitation and the frequency of fire danger was obtained for the forest zone of Russia. The change in fire danger potential was evaluated using temperature and precipitation monthly means at the nodes of a regular spatial grid. Climate change scenarios were obtained from Global Climate Models (GCM) ensemble projections. The maximum increases (about 12–30%) of the number of days with fire danger conditions during the twenty-first century fire season were obtained for the southern forest zone boundary in both the European region of Russia and in Siberia. In the Baikal and Primoriye Regions, fire danger distributions in the twenty-first century are not projected to change significantly.     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

Assessment of climate information needs in the Argentinean Agro-business sector

The present investigation aims at identifying the needs of the Argentinean agro-business sector for climate research and information. Our approach is focused on any type of climate information from past observations to seasonal prediction and climate change projections. We tried, in particular, to evaluate the possible existence of specific research themes that would be of major interest for these agriculture end-users. We interviewed representatives of seventeen companies from three major agrobusiness sectors: (1) regional/national entities such as agricultural associations, cooperatives and commercial trade boards; (2) insurance companies in the Argentinean agro-insurance market; and (3) large national and international companies representing cereal producers, agro-chemistry and agro-seeds. While all the interviewees recognized the strong influence of climate on their activities, they all pointed out that, at the time of making decision, they considered the political and economic risks rather than the climate one. In many aspects, climate is often considered as a fatality against which it is difficult to be protected. An interesting result is the confidence of the private sectors in the climate information provided by public sources (INTA, SMN, Universities) although they contract private consultants for frequent reports and tendencies. Finally, we explain how such sectors could make a better use of climate information (seasonal prediction, climate change scenarios) that could be integrated in their business projections. In particular, the development of new financial contracts (climate derivatives) open in countries with a relatively poor insurance history new ways to protect the different agricultural sectors, from the producer to large companies.     

Climate simulations of major estuarine watersheds in the Mid-Atlantic region of the US

To better understand the implications of anthropogenic climate change for three major Mid-Atlantic estuaries (the Chesapeake Bay, the Delaware Bay, and the Hudson River Estuary), we analyzed the regional output of seven global climate models. The simulation given by the average of the models was generally superior to individual models, which differed dramatically in their ability to simulate twentieth-century climate. The model average had little bias in its mean temperature and precipitation and, except in the Lower Chesapeake Watershed, was able to capture the twentieth-century temperature trend. Weaknesses in the model average were too much seasonality in temperature and precipitation, a shift in precipitation’s summer maximum to spring and winter minimum to fall, interannual variability that was too high in temperature and too low in precipitation, and inability to capture the twentieth-century precipitation increase. There is some evidence that model deficiencies are related to land surface parameterizations. All models warmed over the twenty-first century under the six greenhouse gas scenarios considered, with an increase of 4.7 ± 2.0°C (model mean ± 1 standard deviation) for the A2 scenario (a medium-high emission scenario) over the Chesapeake Bay Watershed by 2070–2099. Precipitation projections had much weaker consensus, with a corresponding increase of 3 ± 12% for the A2 scenario, but in winter there was a more consistent increase of 8 ± 7%. The projected climate averaged over the four best-performing models was significantly cooler and wetter than the projected seven-model-average climate. Precipitation projections were within the range of interannual variability but temperature projections were not. The implied research needs are for improvements in precipitation projections and a better understanding of the impacts of warming on streamflow and estuarine ecology and biogeochemistry.     

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.