Climate change scenarios and long term projections

Predictions of future temperature increases depend critically on the projections of future greenhouse gas emissions. Yet there is a vigorous debate about how these projections should be undertaken. This paper explores a range of methodological issues surrounding projecting greenhouse emissions over the next century. It points out that understanding future emissions requires a framework that deals with the sources of economic growth and allows for endogenous structural change. It also explores the role of convergence assumptions and the “Castles and Henderson Critique” of the Special Report on Emission Scenarios (SRES) regarding use of Market Exchange Rates (MERs) rather than Purchasing Power Parity exchange rates (PPPs) to benchmark income differentials in the world economy. In the G-Cubed multi-country model, we show that emission projections based on convergence assumptions defined in MER terms, are 40% higher by 2100 than emissions generated using a PPP comparison of income differentials between economies. We support the argument presented by Castles and Henderson, that the use of MERs in the SRES represents a serious analytical error. It is not clear what this means for the SRES projections because the SRES is not transparent in its assumptions. In the G-Cubed model, the error leads to considerably higher emissions projections.     

The potential to narrow uncertainty in projections of regional precipitation change

We separate and quantify the sources of uncertainty in projections of regional (～2,500 km) precipitation changes for the twenty-first century using the CMIP3 multi-model ensemble, allowing a direct comparison with a similar analysis for regional temperature changes. For decadal means of seasonal mean precipitation, internal variability is the dominant uncertainty for predictions of the first decade everywhere, and for many regions until the third decade ahead. Model uncertainty is generally the dominant source of uncertainty for longer lead times. Scenario uncertainty is found to be small or negligible for all regions and lead times, apart from close to the poles at the end of the century. For the global mean, model uncertainty dominates at all lead times. The signal-to-noise ratio (S/N) of the precipitation projections is highest at the poles but less than 1 almost everywhere else, and is far lower than for temperature projections. In particular, the tropics have the highest S/N for temperature, but the lowest for precipitation. We also estimate a ‘potential S/N’ by assuming that model uncertainty could be reduced to zero, and show that, for regional precipitation, the gains in S/N are fairly modest, especially for predictions of the next few decades. This finding suggests that adaptation decisions will need to be made in the context of high uncertainty concerning regional changes in precipitation. The potential to narrow uncertainty in regional temperature projections is far greater. These conclusions on S/N are for the current generation of models; the real signal may be larger or smaller than the CMIP3 multi-model mean. Also note that the S/N for extreme precipitation, which is more relevant for many climate impacts, may be larger than for the seasonal mean precipitation considered here.     

Climate change in the northeastern US: regional climate model validation and climate change projections

A high resolution regional climate model (RCM) is used to simulate climate of the recent past and to project future climate change across the northeastern US. Different types of uncertainties in climate simulations are examined by driving the RCM with different boundary data, applying different emissions scenarios, and running an ensemble of simulations with different initial conditions. Empirical orthogonal functions analysis and K-means clustering analysis are applied to divide the northeastern US region into four climatologically different zones based on the surface air temperature (SAT) and precipitation variability. The RCM simulations tend to overestimate SAT, especially over the northern part of the domain in winter and over the western part in summer. Statistically significant increases in seasonal SAT under both higher and lower emissions scenarios over the whole RCM domain suggest the robustness of future warming. Most parts of the northeastern US region will experience increasing winter precipitation and decreasing summer precipitation, though the changes are not statistically significant. The greater magnitude of the projected temperature increase by the end of the twenty-first century under the higher emissions scenario emphasizes the essential role of emissions choices in determining the potential future climate change.     

Climate change projections for the Texas High Plains and Rolling Plains

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

     

Climate change in Central America and Mexico: regional climate model validation and climate change projections

Central America has high biodiversity, it harbors high-value ecosystems and it??s important to provide regional climate change information to assist in adaptation and mitigation work in the region. Here we study climate change projections for Central America and Mexico using a regional climate model. The model evaluation shows its success in simulating spatial and temporal variability of temperature and precipitation and also in capturing regional climate features such as the bimodal annual cycle of precipitation and the Caribbean low-level jet. A variety of climate regimes within the model domain are also better identified in the regional model simulation due to improved resolution of topographic features. Although, the model suffers from large precipitation biases, it shows improvements over the coarse-resolution driving model in simulating precipitation amounts. The model shows a dry bias in the wet season and a wet bias in the dry season suggesting that it??s unable to capture the full range of precipitation variability. Projected warming under the A2 scenario is higher in the wet season than that in the dry season with the Yucatan Peninsula experiencing highest warming. A large reduction in precipitation in the wet season is projected for the region, whereas parts of Central America that receive a considerable amount of moisture in the form of orographic precipitation show significant decreases in precipitation in the dry season. Projected climatic changes can have detrimental impacts on biodiversity as they are spatially similar, but far greater in magnitude, than those observed during the El Ni?o events in recent decades that adversely affected species in the region.     

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.     

Climate change projections over India by a downscaling approach using PRECIS

This study presents a comprehensive assessment of the possible regional climate change over India by using Providing REgional Climates for Impacts Studies (PRECIS), a regional climate model (RCM) developed by Met Office Hadley Centre in the United Kingdom. The lateral boundary data for the simulations were taken from a sub-set of six members sampled from the Hadley Centre’s 17- member Quantified Uncertainty in Model Projections (QUMP) perturbed physics ensemble. The model was run with 25 km × 25 km resolution from the global climate model (GCM) - HadCM3Q at the emission rate of special report on emission scenarios (SRES) A1B scenarios. Based on the model performance, six member ensembles running over a period of 1970-2100 in each experiment were utilized to predict possible range of variations in the future projections for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095) with respect to the baseline period (1975-2005). The analyses concentrated on maximum temperature, minimum temperature and rainfall over the region. For the whole India, the projections of maximum temperature from all the six models showed an increase within the range 2.5°C to 4.4°C by end of the century with respect to the present day climate simulations. The annual rainfall projections from all the six models indicated a general increase in rainfall being within the range 15-24%. Mann-Kendall trend test was run on time series data of temperatures and rainfall for the whole India and the results from some of the ensemble members indicated significant increasing trends. Such high resolution climate change information may be useful for the researchers to study the future impacts of climate change in terms of extreme events like floods and droughts and formulate various adaptation strategies for the society to cope with future climate change.     

Climate change uncertainty: building flexibility into water and flood risk infrastructure

Infrastructure for water, urban drainage and flood protection has a typical lifetime of 30–200 years and its continuing performance is very sensitive to climate change. Investment decisions for such systems are frequently based on state-of-the-art impact assessments using a specified climate change scenario in order to identify a singular optimal adaptive strategy. In a non-stationary world, however, it is risky and/or uneconomic to plan for just one climate change scenario as an average or best estimate, as is done with the use of the Predict-Then-Adapt method. We argue that responsible adaptation requires an alternative method that effectively allows for the lack of knowledge about future climate change by adopting a managed/adaptive strategy. The managed/adaptive strategy confers the ability, derived from built-in flexibility, to adjust to future uncertainties as they unfold. This will restrict the effect of erroneous decisions and help avoid maladaptation. Real In Options (RIO) analysis can facilitate the development of an optimal managed/adaptive strategy to climate change. Here, we show the economic benefits of adopting a managed/adaptive strategy and building in flexibility, using RIO analysis applied for the first time to urban drainage infrastructure.     

Climate change signal analysis for Northeast Asian surface temperature

Climate change detection, attribution, and prediction were studied for the surface temperature in the Northeast Asian region using NCEP/NCAR reanalysis data and three coupled-model simulations from ECHAM4/OPYC3, HadCM3, and CCCma GCMs (Canadian Centre for Climate Modeling and Analysis general circulation model). The Bayesian fingerprint approach was used to perform the detection and attribution test for the anthropogenic climate change signal associated with changes in anthropogenic carbon dioxide (CO2) and sulfate aerosol (SO42-) concentrations for the Northeast Asian temperature. It was shown that there was a weak anthropogenic climate change signal in the Northeast Asian temperature change. The relative contribution of CO2 and SO42- effects to total temperature change in Northeast Asia was quantified from ECHAM4/OPYC3 and CCCma GCM simulations using analysis of variance. For the observed temperature change for the period of 1959-1998, the CO2 effect contributed 10%-21% of the total variance and the di     

Climate change projections for Greek viticulture as simulated by a regional climate model

Theoretical and Applied Climatology - Viticulture represents an important economic activity for Greek agriculture. Winegrapes are cultivated in many areas covering the whole Greek territory, due to...     

Climate change impact assessment on flow regime by incorporating spatial correlation and scenario uncertainty

Flooding risk is increasing in many parts of the world and may worsen under climate change conditions. The accuracy of predicting flooding risk relies on reasonable projection of meteorological data (especially rainfall) at the local scale. The current statistical downscaling approaches face the difficulty of projecting multi-site climate information for future conditions while conserving spatial information. This study presents a combined Long Ashton Research Station Weather Generator (LARS-WG) stochastic weather generator and multi-site rainfall simulator RainSim (CLWRS) approach to investigate flow regimes under future conditions in the Kootenay Watershed, Canada. To understand the uncertainty effect stemming from different scenarios, the climate output is fed into a hydrologic model. The results showed different variation trends of annual peak flows (in 2080–2099) based on different climate change scenarios and demonstrated that the hydrological impact would be driven by the interaction between snowmelt and peak flows. The proposed CLWRS approach is useful where there is a need for projection of potential climate change scenarios.

     

Erratum to: Climate change projections over India by a downscaling approach using PRECIS

The author “Bhaski Bhaskaran” and his affiliation “Fujitsu Laboratory of Europe, Middlesex, UK” should be replaced by “Balakrishnan Bhaskaran”, “Fujitsu Laboratories of Europe Limited, Hayes Park, Middlesex, UK”, respectively.The corrected name and affiliation are shown in this erratum.     

The ocean and climate change policy

The ocean plays a major role in regulating Earth's climate system, and is highly vulnerable to climate change, but continues to receive little attention in the ongoing policymaking designed to mitigate and adapt to global climate change. There are numerous ways to consider the ocean more significantly when developing these policies, several of which offer the co-benefits of biodiversity protection and support of marine-dependent human communities. When developing forward-thinking climate change policy, it is important to understand the ways that the ocean contributes to global climate and to fully inventory the services that the ocean provides to humans. Without more inclusive consideration of the ocean in climate policy, at all levels of governance, policy makers risk weaker than necessary mitigation and adaptation strategies.     

Climate change simulated by full and mixed-layer ocean versions of CSIRO Mk3.5 and Mk3.0: Large-scale sensitivity

The CSIRO Mk3.5 coupled atmosphere-ocean model includes upgrades to atmospheric and oceanic processes that remove a cold bias of the earlier Mk3.0. The global mean warming over the 21st century from Mk3.5 is 3.1 K under the CMIP3 A1B scenario, some 25% larger than that from Mk3.0. Two mixed-layer ocean versions of Mk3.5 were constructed, and these are also more sensitive than Mk3.0. To elucidate these differences, a simple feedback analysis is extended to Mk3.5, using changes for doubled CO₂ in each model version. The net feedback for the low-mid latitude region is the main driver of the sensitivity contrast. The clear-sky component is consistently larger in Mk3.5, as is the increase in specific humidity, even after standardizing by the global warming. Cloud forcing provides a small positive feedback, which is stronger in cases that had larger declines in low-layer cloud. The net positive feedback for the higher-latitude region is larger in the coupled Mk3.5 than Mk3.0, which had more stable Arctic sea ice. However, some contrasts differed among the versions. As for Mk3.0, the surface warming in the coupled Mk3.5 is suppressed over that from the MLO case. Over the ocean, the pattern of suppression is similar to the change in energy flux into the surface in the coupled model. There is also a gradient of equatorial warming in the Asia-Pacific region that relates to the change in net convergence of heat transport by ocean currents. The effect of this pattern on regional rainfall is a focus of Part 2 of the study.     

Probabilistic projections of transient climate change

This paper describes a Bayesian methodology for prediction of multivariate probability distribution functions (PDFs) for transient regional climate change. The approach is based upon PDFs for the equilibrium response to doubled carbon dioxide, derived from a comprehensive sampling of uncertainties in modelling of surface and atmospheric processes, and constrained by multiannual mean observations of recent climate. These PDFs are sampled and scaled by global mean temperature predicted by a Simple Climate Model (SCM), in order to emulate corresponding transient responses. The sampled projections are then reweighted, based upon the likelihood that they correctly replicate observed historical changes in surface temperature, and combined to provide PDFs for 20 year averages of regional temperature and precipitation changes to the end of the twenty-first century, for the A1B emissions scenario. The PDFs also account for modelling uncertainties associated with aerosol forcing, ocean heat uptake and the terrestrial carbon cycle, sampled using SCM configurations calibrated to the response of perturbed physics ensembles generated using the Hadley Centre climate model HadCM3, and other international climate model simulations. Weighting the projections using observational metrics of recent mean climate is found to be as effective at constraining the future transient response as metrics based on historical trends. The spread in global temperature response due to modelling uncertainty in the carbon cycle feedbacks is determined to be about 65–80 % of the spread arising from uncertainties in modelling atmospheric, oceanic and aerosol processes of the climate system. Early twenty-first century aerosol forcing is found to be extremely unlikely to be less than ?1.7 W m^?2. Our technique provides a rigorous and formal method of combining several lines of evidence used in the previous IPCC expert assessment of the Transient Climate Response. The 10th, 50th and 90th percentiles of our observationally constrained PDF for the Transient Climate Response are 1.6, 2.0 and 2.4 °C respectively, compared with the 10–90 % range of 1.0–3.0 °C assessed by the IPCC.