Uncertainty In Climate Change Projections Research Articles

Delaying conservation actions matters for species vulnerable to climate change

Abstract Climate change vulnerability assessments are commonly used to identify species or populations at risk from global climate change, but few translate impact assessments to climate change adaptation actions. Furthermore, most climate change adaptation efforts emphasize where to implement management actions, whereas timing remains largely overlooked. The rate of modern climate change introduces urgency in evaluating whether delaying conservation actions compromises their efficacy for reaching important conservation targets. We evaluated the importance of multiple climate change adaptation strategies including timing of actions on preventing extinctions for a threatened climate‐sensitive species, the Eastern Massasauga rattlesnake (Sistrurus catenatus). We parameterized a range‐wide population viability analysis model that related demographic sensitivities to drought events and human‐modified land cover to assess vulnerability to future climate change. Using simulations, we assessed the efficacy and trade‐offs associated with alternative climate adaptation strategies aimed at maximizing the number of future populations including when to initiate conservation actions, duration of management, number of managed populations, and local management effectiveness. Population‐level projections under future climate change scenarios revealed a broad‐scale pattern of range contraction in the southwestern portion of the current range. Along the extinction gradient, we identified demographic strongholds and refugia critical for population persistence under climate change as well as populations at high risk of extinction and candidates for climate change adaptation actions. In the context of future climate change, the timing of conservation actions was crucial; acting earlier maximized chances of achieving conservation targets. Even considering uncertainty in climate change projections, delaying actions was less efficient and introduced undesirable trade‐offs including the need to implement conservation actions for longer or targeting more populations to achieve a similar conservation target. Synthesis and applications. Our findings highlight how acting quickly reduces risk and improves outcomes for a highly vulnerable species under future climate change. Climate change vulnerability assessments require translation of model‐based outputs into tractable information for climate change adaptation planning. Quantifying trade‐offs associated with the multidimensional decision space related to species conservation and recovery planning is a critical step in climate change adaptation.

Decomposing sources of uncertainty in climate change projections of boreal forest primary production

We are bound to large uncertainties when considering impacts of climate change on forest productivity. Studies formally acknowledging and determining the relative importance of different sources of this uncertainty are still scarce, although the choice of the climate scenario, and e.g. the assumption of the CO2 effects on tree water use can easily result in contradicting conclusions of future forest productivity. In a large scale, forest productivity is primarily driven by two large fluxes, gross primary production (GPP), which is the source for all carbon in forest ecosystems, and heterotrophic respiration. Here we show how uncertainty of GPP projections of Finnish boreal forests divides between input, mechanistic and parametric uncertainty. We used the simple semi-empirical stand GPP and water balance model PRELES with an ensemble of downscaled global circulation model (GCM) projections for the 21st century under different emissions and forcing scenarios (both RCP and SRES). We also evaluated the sensitivity of assumptions of the relationships between atmospheric CO2 concentration (Ca), photosynthesis and water use of trees. Even mean changes in climate projections of different meteorological variables for Finland were so high that it is likely that the primary productivity of forests will increase by the end of the century. The scale of productivity change largely depends on the long-term Ca fertilization effect on GPP and transpiration. However, GCM variability was the major source of uncertainty until 2060, after which emission scenario/pathway became the dominant factor. Large uncertainties with a wide range of projections can make it more difficult to draw ecologically meaningful conclusions especially on the local to regional scales, yet a thorough assessment of uncertainties is important for drawing robust conclusions.

Traiter l'incertitude des projections climatiques (essai)

Handling the uncertainty of climate change projections (essay) The uncertainties associated with climate projections cannot be ignored when those projections are used. They arise from uncertainties surrounding developments for greenhouse gases, uncertainties arising from the climate models and the impact models (hydrology, biodiversity etc.), as well as the natural variability of the climate. To describe these different sources of uncertainty, and take them into account is not easy for engineers and managers, who are more accustomed to reasoning in a determinist framework. This article aims to demonstrate that statistics offers a number of approaches which make it possible to use a given multiscenario, multimodel climate projection. The simplest propose a descriptive summary of the information, while the more complex approaches can test the significance of the expected changes, to separate the different causes of uncertainty and measure each one's contribution to the overall variability. The approach is illustrated by a set of projections for temperature, precipitation and mean snow depth for a mountainous region of the French Alps. It can be easily applied to any variable whose projections have been generated through an impact model which simulates the consequences of a set of climate projections for a given sector defined in social and ecosystem terms.

Effects of uncertain asset stock data on the assessment of climate change risks: A case study of bridge scour in the UK

Bridge owners worldwide manage large numbers of assets with limited budgets through risk assessments, using asset-specific data. However, when managing a large stock of aging assets, maintaining robust and up-to-date data records can be challenging. This issue comes to the fore when trying to understand asset vulnerability to current and future weather events in the context of a changing climate. By using a sample of data on railway bridges in the UK, this paper explores uncertainty associated with raw data used in bridge scour risk assessments for bridge stocks and its interaction with climate change uncertainty. Results indicate that our ability to foresee climate change impacts is not only limited by the aleatory uncertainty of climate change projections; avoidable uncertainty in basic asset data can outweigh aleatory uncertainty by an order of magnitude. Some parameters, such as floodplain width and the width of abutments, were found to be both subject to high uncertainty and also very influential for the estimation of scour risk, leading to reduction in the confidence in scour risk assessments. This finding contrasts with the unchallenged assumption in the field that dimensions of bridge elements are not associated with uncertainty. The nature of scour implies that a potential increase in the frequency and severity of extreme weather events will increase scour risk. This paper shows that in order to be able to understand and account for this increase, scour management processes must effectively address data uncertainty. Active measures to control data quality would be an effective step towards understanding and managing bridge resilience in the context of current and future climatic conditions.

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

Abstract. This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellite-derived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ∼ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ∼ 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

Discrepancy between Ice Particles and Ice Nuclei in Mixed Clouds: Critical Aspects

Measurements of ice crystal concentrations in mixed clouds tend to exceed ice nucleus concentrations measured in nearby clear air. This discrepancy is a source of uncertainty in climate change projections as the radiative properties of mixed phase clouds are largely determined by their liquid and ice water content. The ice enhancement process can sometimes depend on secondary ice production, which can occur through ice crystal fracture during sublimation, cloud drop shattering during freezing or following collision with ice particles. However, the discrepancy is observed even in mixed clouds where only primary ice nucleation processes occur. Several hypotheses have been suggested for the observed discrepancies. One factor could be the existence in clouds of pockets of high vapor supersaturation formed by droplet freezing or removal of small droplets by collision with larger droplets, associated with the fact that ice crystal concentration increases with water supersaturation. However, ice crystal concentrations are usually measured at near water saturation. Additional factors could be drop freezing during evaporation and activation of droplet evaporation residues. Here we suggest that a major factor could be underestimation of the contact freezing mode as it is not measured in experimental campaigns and seldom considered in nucleation models. Laboratory experiments give only incomplete answers to the important questions concerning the contact freezing mode, e.g. what fraction of the aerosol particles that come into contact with the droplet surface results in a freezing event and what is the influence of particle type and size, air temperature and relative humidity. As supercooled droplets grow or evaporate in mixed clouds, phoretic forces should play an important role in the collision efficiency between aerosol and droplets, and consequently in contact freezing. A further question is the possibility that aerosol, usually not active in deposition or condensation/immersion freezing, can trigger ice nucleation by colliding with supercooled droplets.

Are existing biodiversity conservation strategies appropriate in a changing climate?

Many countries have conservation plans for threatened species, but such plans have generally been developed without taking into account the potential impacts of climate change. Here, we apply a decision framework, specifically developed to identify and prioritise climate change adaptation actions and demonstrate its use for 30 species threatened in the UK. Our aim is to assess whether government conservation recommendations remain appropriate under a changing climate. The species, associated with three different habitats (lowland heath, broadleaved woodland and calcareous grassland), were selected from a range of taxonomic groups (primarily moths and vascular plants, but also including bees, bryophytes, carabid beetles and spiders). We compare the actions identified for these threatened species by the decision framework with those included in existing conservation plans, as developed by the UK Government's statutory adviser on nature conservation. We find that many existing conservation recommendations are also identified by the decision framework. However, there are large differences in the spatial prioritisation of actions when explicitly considering projected climate change impacts. This includes recommendations for actions to be carried out in areas where species do not currently occur, in order to allow them to track movement of suitable conditions for their survival. Uncertainties in climate change projections are not a reason to ignore them. Our results suggest that existing conservation plans, which do not take into account potential changes in suitable climatic conditions for species, may fail to maximise species persistence. Comparisons across species also suggest a more habitat-focused approach could be adopted to enable climate change adaptation for multiple species.

Assessment of the Sources of Variation in Changes of Precipitation Characteristics over the Rhine Basin Using a Linear Mixed-Effects Model

Abstract A linear mixed-effects (LME) model is developed to discriminate the sources of variation in the changes of several precipitation characteristics over the Rhine basin as projected by an ensemble of 191 global climate model (GCM) simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The uncertainty in climate change projections originates from natural internal variability, imperfect climate models, and the unpredictability of future greenhouse gas forcing. The LME model allows for the quantification of the contribution of these sources of uncertainty as well as the interaction between greenhouse gas forcing and climate model. In addition, dependence between climate models can be accounted for by using a two-level LME model in which the GCMs are grouped according to their atmospheric circulation model. Statistical models of varied complexity are assessed by the Akaike information criterion. More than 60% of the variance of the changes in mean summer precipitation and various quantiles of 5-day summer precipitation at the end of the twenty-first century can be explained by the climate model. Differences between climate models are also the main source of uncertainty for the changes in three drought characteristics in the summer half-year. In winter, the differences between GCMs are smaller, and natural variability explains a large proportion of the variance of the changes. Natural variability is also the main source of uncertainty for the changes in two indices of extreme precipitation. The contribution of the forcing scenario to the variance of the changes is generally less than 25%.

Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional climate models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction (BC) of RCM temperature and precipitation for Finland is carried out using different versions of the distribution based scaling (DBS) method. The DBS-adjusted RCM data are used as input of a hydrological model to simulate changes in discharges of four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS-adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period 1961–2000 and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

A process-based evaluation of dust-emitting winds in the CMIP5 simulation of HadGEM2-ES

Despite the importance of dust aerosol in the Earth system, state-of-the-art models show a large variety for North African dust emission. This study presents a systematic evaluation of dust emitting-winds in 30 years of the historical model simulation with the UK Met Office Earth-system model HadGEM2-ES for the Coupled Model Intercomparison Project Phase 5. Isolating the effect of winds on dust emission and using an automated detection for nocturnal low-level jets (NLLJs) allow an in-depth evaluation of the model performance for dust emission from a meteorological perspective. The findings highlight that NLLJs are a key driver for dust emission in HadGEM2-ES in terms of occurrence frequency and strength. The annually and spatially averaged occurrence frequency of NLLJs is similar in HadGEM2-ES and ERA-Interim from the European Centre for Medium-Range Weather Forecasts. Compared to ERA-Interim, a stronger pressure ridge over northern Africa in winter and the southward displaced heat low in summer result in differences in location and strength of NLLJs. Particularly the larger geostrophic winds associated with the stronger ridge have a strengthening effect on NLLJs over parts of West Africa in winter. Stronger NLLJs in summer may rather result from an artificially increased mixing coefficient under stable stratification that is weaker in HadGEM2-ES. NLLJs in the Bodélé Depression are affected by stronger synoptic-scale pressure gradients in HadGEM2-ES. Wintertime geostrophic winds can even be so strong that the associated vertical wind shear prevents the formation of NLLJs. These results call for further model improvements in the synoptic-scale dynamics and the physical parametrization of the nocturnal stable boundary layer to better represent dust-emitting processes in the atmospheric model. The new approach could be used for identifying systematic behavior in other models with respect to meteorological processes for dust emission. This would help to improve dust emission simulations and contribute to decreasing the currently large uncertainty in climate change projections with respect to dust aerosol.

Multi-model ensemble analysis of runoff extremes for climate change impact assessments

Summary In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades.

High-resolution distributed analysis of climate and anthropogenic changes on the hydrology of an Alpine catchment

Summary A fully distributed hydrological analysis at scales significant for water management for present-day, and projected future climate conditions is presented for a catchment in the Alps. We selected the upper Rhone basin (Switzerland) as a test case for understanding anthropogenic impacts including climate change on water resources and flood risk in the Alpine area. The upper Rhone basin contains reservoirs, river diversions and irrigated areas offering the opportunity to study the interaction between climate change effects and hydraulic infrastructure. Anthropogenic disturbances of the flow regime were implemented in detail in the hydrological analysis. We downscaled climate model realizations using a methodology that accounts for the uncertainty in climate change projections related to the stochastic variability of precipitation and air temperature. We showed how climate change effects on streamflow propagate from high elevation headwater catchments to the river in the main valley by analyzing changes in several hydrological metrics and at various temporal scales across 297 control sections. Changes in the natural hydrological regime imposed by the existing hydraulic infrastructure are likely larger than climate change signals expected by the middle of the 21st century in most of the river network. Despite a strong uncertainty induced by stochastic climate variability, we identified an elevational dependence of climate change impacts with a severe reduction in streamflow due to the missing contribution of water from ice melt at high-elevation and a dampened effect downstream. Reduced ice cover and ice melt are likely to have significant implications for hydropower production. The impacts can emerge without any additional climate warming. A decrease of August–September discharge and an increase of hourly and daily maximum flows appear as plausible projected change for the most part of the catchment. However, it is unlikely that major changes in total runoff for the entire upper Rhone basin will occur in the next four decades.

Atmospheric circulation as a source of uncertainty in climate change projections

Scientific confidence in climate change effects is much higher for aspects related to global patterns of surface temperature, than for circulation. Circulation will remain hard to predict, necessitating a risk-based approach to decision making.

Obstacles to climate change adaptation decisions: a case study of sea-level rise and coastal protection measures in Kiribati

International aid is increasingly focused on adaptation to climate change. At recent meetings of the parties to the United Nations Framework Convention on Climate Change, the developed world agreed to rapidly increase international assistance to help the developing world respond to the impacts of climate change. In this paper, we examine the decision-making challenges facing internationally supported climate change adaptation projects, using the example of efforts to implement coastal protection measures (e.g. sea walls, mangrove planting) in Kiribati. The central equatorial Pacific country is home to the Kiribati Adaptation Project, the first national-level climate change adaptation project supported by the World Bank. Drawing on interview and document research conducted over an 8-year period, we trace the forces influencing decisions about coastal protection measures, starting from the variability and uncertainty in climate change projections, through the trade-offs between different measures, to the social, political, and economic context in which decisions are finally made. We then discuss how sub-optimal adaptation measures may be implemented despite years of planning, consultation, and technical studies. This qualitative analysis of the real-world process of climate change adaptation reveals that embracing a culturally appropriate and short-term (~20 years) planning horizon, while not ignoring the longer-term future, may reduce the influence of scientific uncertainty on decisions and provide opportunities to learn from mistakes, reassess the science, and adjust suboptimal investments. The limiting element in this approach to adaptation is likely to be the availability of consistent, long-term financing.

Modelling shifts in agroclimate and crop cultivar response under climate change

This paper aims: (i) to identify at national scale areas where crop yield formation is currently most prone to climate-induced stresses, (ii) to evaluate how the severity of these stresses is likely to develop in time and space, and (iii) to appraise and quantify the performance of two strategies for adapting crop cultivation to a wide range of (uncertain) climate change projections. To this end we made use of extensive climate, crop, and soil data, and of two modelling tools: N-AgriCLIM and the WOFOST crop simulation model. N-AgriCLIM was developed for the automatic generation of indicators describing basic agroclimatic conditions and was applied over the whole of Finland. WOFOST was used to simulate detailed crop responses at four representative locations. N-AgriCLIM calculations have been performed nationally for 3829 grid boxes at a 10 × 10 km resolution and for 32 climate scenarios. Ranges of projected shifts in indicator values for heat, drought and other crop-relevant stresses across the scenarios vary widely – so do the spatial patterns of change. Overall, under reference climate the most risk-prone areas for spring cereals are found in south-west Finland, shifting to south-east Finland towards the end of this century. Conditions for grass are likely to improve. WOFOST simulation results suggest that CO2 fertilization and adjusted sowing combined can lead to small yield increases of current barley cultivars under most climate scenarios on favourable soils, but not under extreme climate scenarios and poor soils. This information can be valuable for appraising alternative adaptation strategies. It facilitates the identification of regions in which climatic changes might be rapid or otherwise notable for crop production, requiring a more detailed evaluation of adaptation measures. The results also suggest that utilizing the diversity of cultivar responses seems beneficial given the high uncertainty in climate change projections.

Comparison of climate change signals in CMIP3 and CMIP5 multi-model ensembles and implications for Central Asian glaciers

Abstract. Central Asian water resources largely depend on melt water generated in the Pamir and Tien Shan mountain ranges. To estimate future water availability in this region, it is necessary to use climate projections to estimate the future glacier extent and volume. In this study, we evaluate the impact of uncertainty in climate change projections on the future glacier extent in the Amu and Syr Darya river basins. To this end we use the latest climate change projections generated for the upcoming IPCC report (CMIP5) and, for comparison, projections used in the fourth IPCC assessment (CMIP3). With these projections we force a regionalized glacier mass balance model, and estimate changes in the basins' glacier extent as a function of the glacier size distribution in the basins and projected temperature and precipitation. This glacier mass balance model is specifically developed for implementation in large scale hydrological models, where the spatial resolution does not allow for simulating individual glaciers and data scarcity is an issue. Although the CMIP5 ensemble results in greater regional warming than the CMIP3 ensemble and the range in projections for temperature as well as precipitation is wider for the CMIP5 than for the CMIP3, the spread in projections of future glacier extent in Central Asia is similar for both ensembles. This is because differences in temperature rise are small during periods of maximum melt (July–September) while differences in precipitation change are small during the period of maximum accumulation (October–February). However, the model uncertainty due to parameter uncertainty is high, and has roughly the same importance as uncertainty in the climate projections. Uncertainty about the size of the decline in glacier extent remains large, making estimates of future Central Asian glacier evolution and downstream water availability uncertain.

Climate Change from 1850 to 2005 Simulated in CESM1(WACCM)

Abstract The NCAR Community Earth System Model (CESM) now includes an atmospheric component that extends in altitude to the lower thermosphere. This atmospheric model, known as the Whole Atmosphere Community Climate Model (WACCM), includes fully interactive chemistry, allowing, for example, a self-consistent representation of the development and recovery of the stratospheric ozone hole and its effect on the troposphere. This paper focuses on analysis of an ensemble of transient simulations using CESM1(WACCM), covering the period from the preindustrial era to present day, conducted as part of phase 5 of the Coupled Model Intercomparison Project. Variability in the stratosphere, such as that associated with stratospheric sudden warmings and the development of the ozone hole, is in good agreement with observations. The signals of these phenomena propagate into the troposphere, influencing near-surface winds, precipitation rates, and the extent of sea ice. In comparison of tropospheric climate change predictions with those from a version of CESM that does not fully resolve the stratosphere, the global-mean temperature trends are indistinguishable. However, systematic differences do exist in other climate variables, particularly in the extratropics. The magnitude of the difference can be as large as the climate change response itself. This indicates that the representation of stratosphere–troposphere coupling could be a major source of uncertainty in climate change projections in CESM.

Uncertainty in Climate Change Projections of the Hadley Circulation: The Role of Internal Variability

Abstract The uncertainty arising from internal climate variability in climate change projections of the Hadley circulation (HC) is presently unknown. In this paper it is quantified by analyzing a 40-member ensemble of integrations of the Community Climate System Model, version 3 (CCSM3), under the Special Report on Emissions Scenarios (SRES) A1B scenario over the period 2000–60. An additional set of 100-yr-long time-slice integrations with the atmospheric component of the same model [Community Atmosphere Model, version 3.0 (CAM3)] is also analyzed. Focusing on simple metrics of the HC—its strength, width, and height—three key results emerge from the analysis of the CCSM3 ensemble. First, the projected weakening of the HC is almost entirely confined to the Northern Hemisphere, and is stronger in winter than in summer. Second, the projected widening of the HC occurs only in the winter season but in both hemispheres. Third, the projected rise of the tropical tropopause occurs in both hemispheres and in all seasons and is, by far, the most robust of the three metrics. This paper shows further that uncertainty in future trends of the HC width is largely controlled by extratropical variability, while those of HC strength and height are associated primarily with tropical dynamics. Comparison of the CCSM3 and CAM3 integrations reveals that ocean–atmosphere coupling is the dominant source of uncertainty in future trends of HC strength and height and of the tropical mean meridional circulation in general. Finally, uncertainty in future trends of the hydrological cycle is largely captured by the uncertainty in future trends of the mean meridional circulation.

Why is the amplitude of the Indian Ocean Dipole overly large in CMIP3 and CMIP5 climate models?

The Indian Ocean Dipole (IOD) affects weather and climate in many parts of the world, but a realistic simulation of the IOD in state‐of‐the‐art climate models remains a challenge. In most models, IOD peak‐season amplitudes are systematically larger than that of the observed, a bias that deterministically affects climate projections in IOD‐affected regions. Understanding the cause of this bias is therefore essential for alleviating model errors and reducing uncertainty in climate projections. Here it is shown that most Coupled Model Intercomparison Project Phase Three (CMIP3) and CMIP5 models produce too strong a Bjerknes feedback in the equatorial Indian Ocean, leading to the IOD bias. The thermocline‐sea surface temperature (SST) feedback exerts the strongest influence on the simulated IOD amplitude; models simulating a stronger thermocline‐SST feedback systematically generate a greater IOD amplitude. The strength of the thermocline‐SST feedback in most models is predominantly controlled by the climatological west‐east slope of the equatorial thermocline, which features an unrealistic mean slope tilting upward toward the eastern Indian Ocean. The unrealistic thermocline structure is accompanied by too strong a mean easterly wind and an overly strong west‐minus‐east SST gradient. The linkage of the mean climatic conditions, feedback strength, and projected climate highlights the fundamental importance of realistically simulating these components of the climate system for reducing uncertainty in climate change projections in IOD‐affected regions.

Influence of Drought on Salamander Occupancy of Isolated Wetlands on the Southeastern Coastal Plain of the United States

In the southeastern U.S., changes in temperature and precipitation over the last three decades have been the most dramatic in winter and spring seasons. Continuation of these trends could negatively impact pond-breeding amphibians, especially those that rely on winter and spring rains to fill seasonal wetlands, trigger breeding, and ensure reproductive success. From 2009 to 2012, we monitored Spring and Fall presence of aquatic stages (larval and paedomorphic, gilled adult) of a winter-breeding amphibian (the mole salamander, Ambystoma talpoideum) and used multi-season models to estimate occupancy, local colonization and extinction. Seasonal estimates of occupancy, corrected for imperfect detection, declined from 22.3 % of ponds in Spring 2009 to 9.9 % in Fall 2012. Our best supported model suggested that changes in occupancy were driven by increased rates of extinction that corresponded with drought-related drying of ponds. Based on uncertainty in climate change projections for the Southeast, we present a conceptual model of predicted changes in wetland hydroperiods across a landscape with projected decreases and increases in future precipitation. Such precipitation changes could alter wetland hydroperiods, facilitate extinctions of species adapted to short, intermediate or long hydroperiod environments and, ultimately, modify the composition of amphibian communities within freshwater wetland ecosystems.