Uncertainty In Future Climate Change Research Articles

Real options analysis for urban flood mitigation under environmental change

As urban flooding becomes more frequent and severe due to climate change and accelerated urbanization, effective urban flood control measures are needed to mitigate urban flooding under environmental change. However, few studies consider both climate change and land use change and formulate urban flood control plans for the uncertainty of climate change. This study proposes a real options method based on trinomial tree model to design urban flood control measures under environmental changes. Future rainfall was predicted based on the SDSM model. Land use changes for 2035 and 2050 were predicted using the CA-Markov model. Considering the uncertainty of future climate change, future urban flood control measures based on the real options method were developed, and to compare and analyze them with fixed investment method. The research results show that rainfall and construction land in the study area will increase in the future. The benefit of real options retrofit scheme is higher than the fixed investment retrofit scheme and climate change uncertainty has a greater impact on flood control planning decisions. The research framework proposed in this paper provides an effective basis for the formulation of urban flood control strategies and the design of flood control measures under environmental change.

Dynamic Response and Adaptation of Grassland Ecosystems in the Three-River Headwaters Region under Changing Environment: A Review.

The Three-River Headwaters Region (TRHR) is crucial to the sustainable development of China and Southeast Asia. The sustainability of grassland ecosystems in the region has been seriously challenged in recent years. This paper reviewed the changes in the grasslands of the TRHR and their responses to climate change and human activities. The review showed that accurate monitoring of grassland ecological information is the basis for effective management. Although alpine grassland coverage and the above-ground biomass of the alpine grassland have generally increased in the region over the past 30 years, the degradation has not been fundamentally curbed. Grassland degradation substantially reduced topsoil nutrients and affected their distribution, deteriorated soil moisture conditions, and aggravated soil erosion. Grassland degradation led to loss of productivity and species diversity, and this is already harming the well-being of pastoralists. The "warm and wet" trend of the climate promoted the restoration of alpine grasslands, but widespread overgrazing is considered as one of the main reasons for grassland degradation, and related differences still exist. Since 2000, the grassland restoration policy has achieved fruitful results, but the formulation of the policy still needs to integrate market logic effectively and strengthen the understanding of the relationship between ecological protection and cultural protection. In addition, appropriate human intervention mechanisms are urgently needed due to the uncertainty of future climate change. For mildly and moderately degraded grassland, traditional methods are applicable. However, the severely degraded "black soil beach" needs to be restored by artificial seeding, and the stability of the plant-soil system needs to be emphasized to establish a relatively stable community to prevent secondary degradation.

Estimated cloud-top entrainment index explains positive low-cloud-cover feedback

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming: The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using state-of-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes.

All roads lead to retreat: adapting to sea level rise using a trigger-based pathway

ABSTRACT As coastal communities all over the world deal with the consequences of rising sea-level and more intense storm events, planners and managers continue to grapple with the optimal policy approach to managing increasing risks to coastal ecosystems, people and property. In this article, we describe a flexible pathway approach to adaptation derived in south west Australia for local government. While the issue is usually addressed using a conventional option analysis of the ‘retreat-accommodate-protect’ alternatives within a given timeframe (often until 2100), we argue that this approach is misleading in that it obfuscates the long term realities of climate change. Sea-level will be rising for hundreds if not thousands of years, meaning that retreat is ultimately inevitable for any coastlines currently determined to be vulnerable – the only uncertainty is when this will be necessary. Accordingly the focus should be on continual monitoring, updated hazard mapping, and the identification of sequential triggers that regulate land uses. We propose this simplified flexible pathway as a rational approach to dealing with the temporal uncertainty of future climate change that should be widely adopted.

Impact of oceans on climate change in drylands

Drylands account for approximately 41% of the global total land area. Significant warming and rare precipitation in drylands result in a fragile ecology and deterioration of the living environment, making it more sensitive to global climate change. As an important regulator of the Earth’s climate system, the oceans play a vital role in the process of climate change in drylands. In modern climate change in particular, the impact of marine activities on climate change in drylands cannot be neglected. This paper reviews the characteristics of climate change in drylands over the past 100 years, and summarizes the researches conducted on the impact of marine activities on these changes. The review focuses on the impact of the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), El Nino and La Nina on climate change in drylands, and introduces the mechanisms by which different oceanic oscillation factors synergistically affect climate change in drylands. Studies have shown that global drylands have experienced a significant intensification in warming in the past 100 years, which shows obvious characteristics of interdecadal dry/wet variations. The characteristics of these changes are closely related to the oscillatory factors of the oceanic interdecadal scale. Different phase combinations of oceanic oscillation factors significantly change the land-sea thermal contrast, which in turn affects the westerly jet, planetary wave and blocking frequency, resulting in changes in the temperature and dry/wet characteristics of drylands. With the intensification of climate change in drylands, the impact of marine activities on these regions will reveal new characteristics in the future, which will increase the uncertainty of future climate change in drylands and intensify the impact of these drylands on global climate.

Potential impacts of climate change on sediment yield in the Xunhe River basin

Sediment yield is an important issue in climate impact studies. Potential impact of climate change on sediment yield in the Xunhe River basin is investigated using a combination of climate, hydrological, and sediment yield models. Twenty general circulation methods under representative concentration pathways (RCPs) 4.5/8.5 are downscaled using the quantile mapping method to reduce the uncertainty in future climate change. The lumped Xin9anjiang (XAJ) hydrological model and the conceptual sediment yield model are then used to simulate the runoff and sediment transport process in the case of future climate change. The model performance is influenced by the selection of the calibration data set, which increases the simulation uncertainty. Furthermore, the input data, model structure, and parameters should all be considered sensibly, as they also increase uncertainty. The evaporation coefficient <i>CKE</i> in the XAJ hydrological model is found to be the most sensitive parameter according to the multiparametric sensitivity analysis method. In addition, the maximum slope sediment concentration <i>CM</i> and the mean gully sediment concentration <i>CGM</i> in the conceptual sediment yield model are found to be relatively sensitive parameters. The aforementioned sensitive parameters are closely related to climate change. Under RCP4.5/8.5 scenarios, the average annual precipitation, runoff, and sediment yield might tend to increase in the future with a larger increase under RCP8.5. The average monthly sediment yield is less sensitive to climate change than the average monthly runoff. Future precipitation, runoff, and sediment yield display varying tendencies in different months owing to seasonal variation characteristics. These results suggest that future sediment yield might increase under the influences of climate and runoff change, which will serve as a guideline for sediment resource management.

Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models

Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts assessments.

Protecting Energy Infrastructure against the Uncertainty of Future Climate Change: A Real Options Approach

The coastal impacts of climate change, including flooding and erosion due to storms and sea-level rise, and the possible adaptation responses have been studied using very different approaches; from very detailed site-specific, process-based investigations and interventions to global macroeconomic assessments of coastal zone vulnerability. This paper presents a flood defense real option analysis methodology that values potential investment decisions made in the building and maintaining of flood defenses around electricity infrastructure at local spatial scales for a large region. Real option analysis embraces uncertainty in future climate conditions and flexibility in the management of investment projects to produce a more precise optimal outcome than attained with traditional discount cash flow analysis alone. The method uses high-level analysis from flood inundation models to assess the cost of flooding for energy infrastructure at the present-day up to the highest plausible sea-level rise for the UK in 2100 known as the H++ scenario, which projects a sea-level rise of 1.8 m. These costs feed into a real option valuation model able to identify which energy infrastructure will benefit from investment, and when. This northwest UK study identifies two infrastructure sites that, today, would benefit from flood defence investment over discount cash flow analysis, increasing to an additional 14 in 2050. Using this method has identified 46 sites that would benefit from deferring flood defence investment now, reducing to 35 sites in 2050. This method of project valuation can be applied to any feature within the floodplain, e.g. infrastructure or residential housing, making it an adaptable and useful tool in identifying vulnerable features that require investment to ensure they stay resilient to extreme flood events in the future. This work is the result of an inter-disciplinary collaboration between hydrodynamic modelling, flood risk assessment and economics. The outputs of which are ideal to be fed into a decision-support tool, allowing stakeholders to interrogate and disseminate information about the spatial locations they are interested in.

Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

The purpose of this study is to identify the relationship between weather variables and buildings damaged in natural disasters. We used four datasets on building damage history and 33 weather datasets from 230 regions in South Korea in a decision tree analysis to evaluate the risk of building damage. We generated the decision tree model to determine the risk of rain, gale, and typhoon (excluding gale with less damage). Using the weight and limit values of the weather variables derived using the decision tree model, the risk of building damage was assessed for 230 regions in South Korea until 2100. The number of regions at risk of rain damage increased by more than 30% on average. Conversely, regions at risk of damage from snowfall decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, while those at high risk of the same increased by up to 62.5% under RCP 8.5. The results of this study are highly fluid since they are based on the uncertainty of future climate change. However, the study is meaningful because it suggests a new method for assessing disaster risk using weather indices.

Clouds, Circulation, and Climate Sensitivity in a Radiative‐Convective Equilibrium Channel Model

AbstractTropical cloud and circulation changes are large sources of uncertainty in future climate change. This problem owes partly to the scale separation between large‐scale tropical dynamics ( km) and convective dynamics ( km), which generally requires parameterizing convection in models that resolve large‐scale dynamics, or parameterizing (or omitting) large‐scale dynamics in models that permit convection. Here we discuss simulations of radiative‐convective equilibrium (RCE) across a wide range of surface temperatures in long‐channel geometry—where the domain size and resolution marginally resolve both large‐scale dynamics and convection. Self‐aggregation of convection in these simulations spontaneously produces realistic dynamical regimes of large‐scale vertical motion. The circulation weakens with surface warming but changes in the degree of self‐aggregation depend on the metric that is used; there is no obvious trend in aggregation with warming. Surface warming causes an upward shift and decrease in area of high clouds, and a sharp decline in midlevel clouds, but no systematic trend in low cloud cover. We introduce a method for approximate radiative kernel feedback analysis in RCE, and apply it to both simulations in long‐channel geometry and in a smaller square domain. The kernel‐corrected cloud feedback is positive but its magnitude varies across temperatures. Compared to simulations that do not have aggregation, there is a more negative net feedback due to the effects of aggregation on relative humidity and cloud cover. These results are consistent with the hypothesis that self‐aggregation moderately reduces climate sensitivity.

Projecting species' vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.

Conceptualization and implementation of ecosystems-based adaptation

While ecosystems-based adaptation (EbA) has been received with great interest, the requirements for EbA implementation and its precise benefits under future climate change are unclear. Furthermore, EbA’s overlap with environmental, and development policy agendas leads to ambiguity regarding what actions fall under the rubric of EbA. We analyze the projects identified by the UNFCCC as examples of EbA to understand how EbA is conceptualized and promoted by the international community. Addressing climate change is the primary objective of 58% of the EbA projects; the other 42% of projects provide adaptation benefits yet are not primarily driven by climate change. A project’s adaptation targeting is tied to its information needs. Projects whose primary objective is to address climate change are more likely to use detailed climate projections than projects whose primary objective is to address natural hazards, development or ecosystems degradation. A majority of projects do not address uncertainty in future climate change or in adaptation benefits, nor do they track adaptation outcomes. This prevalent lack of monitoring highlights the possibility of a gap between expected and realized adaptation outcomes. It also represents a lost opportunity for improving knowledge of the thresholds of effectiveness of EbA and of factors influencing EbA efficacy.

Quantifying the Carbon Balance of Forest Restoration and Wildfire under Projected Climate in the Fire-Prone Southwestern US.

Climate projections for the southwestern US suggest a warmer, drier future and have the potential to impact forest carbon (C) sequestration and post-fire C recovery. Restoring forest structure and surface fire regimes initially decreases total ecosystem carbon (TEC), but can stabilize the remaining C by moderating wildfire behavior. Previous research has demonstrated that fire maintained forests can store more C over time than fire suppressed forests in the presence of wildfire. However, because the climate future is uncertain, I sought to determine the efficacy of forest management to moderate fire behavior and its effect on forest C dynamics under current and projected climate. I used the LANDIS-II model to simulate carbon dynamics under early (2010–2019), mid (2050–2059), and late (2090–2099) century climate projections for a ponderosa pine (Pinus ponderosa) dominated landscape in northern Arizona. I ran 100-year simulations with two different treatments (control, thin and burn) and a 1 in 50 chance of wildfire occurring. I found that control TEC had a consistent decline throughout the simulation period, regardless of climate. Thin and burn TEC increased following treatment implementation and showed more differentiation than the control in response to climate, with late-century climate having the lowest TEC. Treatment efficacy, as measured by mean fire severity, was not impacted by climate. Fire effects were evident in the cumulative net ecosystem exchange (NEE) for the different treatments. Over the simulation period, 32.8–48.9% of the control landscape was either C neutral or a C source to the atmosphere and greater than 90% of the thin and burn landscape was a moderate C sink. These results suggest that in southwestern ponderosa pine, restoring forest structure and surface fire regimes provides a reasonable hedge against the uncertainty of future climate change for maintaining the forest C sink.

Engineering chloroplasts to improve Rubisco catalysis: prospects for translating improvements into food and fiber crops.

494 I. 495 II. 496 III. 496 IV. 499 V. 499 VI. 501 VII. 501 VIII. 502 IX. 505 X. 506 507 References 507 SUMMARY: The uncertainty of future climate change is placing pressure on cropping systems to continue to provide stable increases in productive yields. To mitigate future climates and the increasing threats against global food security, new solutions to manipulate photosynthesis are required. This review explores the current efforts available to improve carbon assimilation within plant chloroplasts by engineering Rubisco, which catalyzes the rate-limiting step of CO2 fixation. Fixation of CO2 and subsequent cycling of 3-phosphoglycerate through the Calvin cycle provides the necessary carbohydrate building blocks for maintaining plant growth and yield, but has to compete with Rubisco oxygenation, which results in photorespiration that is energetically wasteful for plants. Engineering improvements in Rubisco is a complex challenge and requires an understanding of chloroplast gene regulatory pathways, and the intricate nature of Rubisco catalysis and biogenesis, to transplant more efficient forms of Rubisco into crops. In recent times, major advances in Rubisco engineering have been achieved through improvement of our knowledge of Rubisco synthesis and assembly, and identifying amino acid catalytic switches in the L-subunit responsible for improvements in catalysis. Improving the capacity of CO2 fixation in crops such as rice will require further advances in chloroplast bioengineering and Rubisco biogenesis.

상세화된 CMIP5 기후변화전망의 다중모델앙상블 접근에 의한 농업기후지수 평가

The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on , plant period based on , crop period based on , and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

Robust adaptation assessment – climate change and water supply

PurposeThis paper aims to develop a framework to assist the identification of robust adaptation options that account for uncertainty in future climate change impacts for the water sector.Design/methodology/approachThe water evaluation and planning (WEAP) tool, is to identify future water resource vulnerability in the Glore sub‐catchment within the Moy catchment in the West of Ireland. Where water stress is evident, a detailed hydrological modelling approach is developed to enable an assessment of the robustness to uncertainty of future adaptation decisions. WEAP is coupled with a rainfall runoff model (hydrological simulation model), and forced using climate scenarios, statistically downscaled from three global climate models to account for the key sources of uncertainty. While hydrological models are widely applied, they are subject to uncertainties derived from model structure and the parameterisation of the catchment. Here, random sampling of key parameters is employed to incorporate uncertainty from the hydrological modelling process. Behavioural parameter sets are used to generate multiple future streamflow series to determine where the bounds within future hydrological regimes may lie and the ranges within which future adaptation policy pathways need to function.FindingsThis framework allows the identification of adaptation options that are robust to uncertainty in future simulations.Research limitations/implicationsFuture research will focus on the development of more site‐specific adaptation options including soft and hard adaptation strategies. This approach will be applied to multiple water resource regions within Ireland.Originality/valueA robust adaptation assessment decreases the risk of expensive and/or mal‐adaptations in a critical sector for society, the economy and the aquatic environment.

Uncertainty in climate change projections: the role of internal variability

Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes via atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005–2060 in the CMIP3 multi-model ensemble.

Climate change impacts and adaptation in cities: a review of the literature

Many of the decisions relating to future urban development require information on climate change risks to cities This review of the academic and “grey” literature provides an overview assessment of the state of the art in the quantification and valuation of climate risks at the city-scale. We find that whilst a small number of cities, mostly in OECD countries, have derived quantitative estimates of the costs of climate change risks under alternative scenarios, this form of analysis is in its infancy. The climate risks most frequently addressed in existing studies are associated with sea-level rise, health and water resources. Other sectors such as energy, transport, and built infrastructure remain less studied. The review has also undertaken a case study to examine the progress in two cities—London and New York—which are relatively advanced in the assessment of climate risks and adaptation. The case studies show that these cities have benefited from stakeholder engagement at an early stage in their risk assessments. They have also benefited from the development of specific institutional responsibilities for co-ordinating such research from the outset. This involvement has been critical in creating momentum and obtaining resources for subsequent in-depth analysis of sectoral impacts and adaptation needs..While low cost climate down-scaling applications would be useful in future research, the greatest priority is to develop responses that can work within the high future uncertainty of future climate change, to build resilience and maintain flexibility. This can best be used within the context of established risk management practices.

Challenges in Combining Projections from Multiple Climate Models

Abstract Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal—for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate “good” and “bad” models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.

Statistical Calibration of Climate System Properties

Summary The behaviour of modern climate system simulators is controlled by numerous parameters. By matching model outputs with observed data we can perform inference on such parameters. This is a calibration problem that usually requires the ability to evaluate the computer code at any given configuration of the parameters. As the climate system simulator attempts to describe very complex physical phenomena, the task of running the model is very computationally demanding. Thus, a statistical model is required to approximate the model output. In this work, we use output from the Massachusetts Institute of Technology two-dimensional climate model (MIT2DCM), historical records and output from a three-dimensional climate model, to obtain estimates of the climate sensitivity, the effective thermal diffusivity in the deep ocean and the net aerosol forcing that control MIT2DCM. We use a Bayesian approach that allows for the use of scientifically based information on the climate parameters to be used in the calibration process. The model tackles the problem of dealing with multivariate computer model output and incorporates all estimation uncertainties into the posterior distributions of the climate parameters. Additionally we obtain estimates of the correlation structure of the unforced variability of temperature change patterns. These results are critical for understanding uncertainty in future climate change and provide an independent check that the information that is contained in recent climate change is robust to statistical treatment. These results include uncertainties in the estimation of the multivariate covariance matrices.