Toward a new generation of world climate research and computing facilities

The impending threat of global climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather prediction and in our understanding and ability to model the climate system suggest that it is both necessary and possible to revolutionize climate prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the climate system, and because the regional manifestations of climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requirements to predict its behavior reliably are so enormous that the nations of the world should create a small number of multinational high-performance computing facilities dedicated to the grand challenges of developing the capabilities to predict climate variability and change on both global and regional scales over the coming decades. Such facilities will play a key role in the development of next-generation climate models, build global capacity in climate research, nurture a highly trained workforce, and engage the global user community, policy-makers, and stakeholders. We recommend the creation of a small number of multinational facilities with computer capability at each facility of about 20 peta-flops in the near term, about 200 petaflops within five years, and 1 exaflop by the end of the next decade. Each facility should have sufficient scientific workforce to develop and maintain the software and data analysis infrastructure. Such facilities will enable questions of what resolution, both horizontal and vertical, in atmospheric and ocean models, is necessary for more confident predictions at the regional and local level. Current limitations in computing power have placed severe limitations on such an investigation, which is now badly needed. These facilities will also provide the world's scientists with the computational laboratories for fundamental research on weather–climate interactions using 1-km resolution models and on atmospheric, terrestrial, cryospheric, and oceanic processes at even finer scales. Each facility should have enabling infrastructure including hardware, software, and data analysis support, and scientific capacity to interact with the national centers and other visitors. This will accelerate our understanding of how the climate system works and how to model it. It will ultimately enable the climate community to provide society with climate predictions, which are based on our best knowledge of science and the most advanced technology.

Describing Earth system simulations with the Metafor CIM

The Metafor project has developed a common information model (CIM) using the ISO19100 series for- malism to describe numerical experiments carried out by the Earth system modelling community, the models they use, and the simulations that result. Here we describe the mechanism by which the CIM was developed, and its key properties. We introduce the conceptual and application ver- sions and the controlled vocabularies developed in the con- text of supporting the fifth Coupled Model Intercomparison Project (CMIP5). We describe how the CIM has been used in experiments to describe model coupling properties and de- scribe the near term expected evolution of the CIM.

Importance of density-compensated temperature change for deep North Atlantic Ocean heat uptake

The efficiency with which the oceans take up heat has a significant influence on the rate of global warming. Warming of the ocean above 700 m over the past few decades has been well documented. However, most of the ocean lies below 700 m. Here we analyse observations of heat uptake into the deep North Atlantic. We find that the extratropical North Atlantic as a whole warmed by 1.45±0.5×1022 J between 1955 and 2005, but Lower North Atlantic Deep Water cooled, most likely as an adjustment from an early twentieth-century warm period. In contrast, the heat content of Upper North Atlantic Deep Water exhibited strong decadal variability. We demonstrate and quantify the importance of density-compensated temperature anomalies for long-term heat uptake into the deep North Atlantic. These anomalies form in the subpolar gyre and propagate equatorwards. High salinity in the subpolar gyre is a key requirement for this mechanism. In the past 50 years, suitable conditions have occurred only twice: first during the 1960s and again during the past decade. We conclude that heat uptake through density-compensated temperature anomalies will contribute to deep ocean heat uptake in the near term. In the longer term, the importance of this mechanism will be determined by competition between the multiple processes that influence subpolar gyre salinity in a changing climate.

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near-term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.

Reliability of regional climate model trends

A necessary condition for a good probabilistic forecast is that the forecast system is shown to be reliable: forecast probabilities should equal observed probabilities verified over a large number of cases. As climate change trends are now emerging from the natural variability, we can apply this concept to climate predictions and compute the reliability of simulated local and regional temperature and precipitation trends (1950–2011) in a recent multi-model ensemble of climate model simulations prepared for the Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5). With only a single verification time, the verification is over the spatial dimension. The local temperature trends appear to be reliable. However, when the global mean climate response is factored out, the ensemble is overconfident: the observed trend is outside the range of modelled trends in many more regions than would be expected by the model estimate of natural variability and model spread. Precipitation trends are overconfident for all trend definitions. This implies that for near-term local climate forecasts the CMIP5 ensemble cannot simply be used as a reliable probabilistic forecast.

Decadal climate prediction: an update from the trenches

This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about six to nine years. Recent multi-model results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multi-model ensemble decadal hindcasts than in single model results, with multi-model initialized predictions for near term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6-9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño-Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.

Addressing uncertainty in adaptation planning for agriculture

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

Tackling regional climate change by leaf albedo bio-geoengineering

The likelihood that continuing greenhouse-gas emissions will lead to an unmanageable degree of climate change [1] has stimulated the search for planetary-scale technological solutions for reducing global warming [2] (“geoengineering”), typically characterized by the necessity for costly new infrastructures and industries [3]. We suggest that the existing global infrastructure associated with arable agriculture can help, given that crop plants exert an important influence over the climatic energy budget 4 and 5 because of differences in their albedo (solar reflectivity) compared to soils and to natural vegetation [6]. Specifically, we propose a “bio-geoengineering” approach to mitigate surface warming, in which crop varieties having specific leaf glossiness and/or canopy morphological traits are specifically chosen to maximize solar reflectivity. We quantify this by modifying the canopy albedo of vegetation in prescribed cropland areas in a global-climate model, and thereby estimate the near-term potential for bio-geoengineering to be a summertime cooling of more than 1°C throughout much of central North America and midlatitude Eurasia, equivalent to seasonally offsetting approximately one-fifth of regional warming due to doubling of atmospheric CO2[7]. Ultimately, genetic modification of plant leaf waxes or canopy structure could achieve greater temperature reductions, although better characterization of existing intraspecies variability is needed first.

Bounding the role of black carbon in the climate system: a scientific assessment

Black carbon aerosol plays a unique and important role in Earth’s climate system. Black carbon is a type of carbonaceous material with a unique combination of physical properties. This assessment provides an evaluation of black-carbon climate forcing that is comprehensive in its inclusion of all known and relevant processes and that is quantitative in providing best estimates and uncertainties of the main forcing terms: direct solar absorption; influence on liquid, mixed phase, and ice clouds; and deposition on snow and ice. These effects are calculated with climate models, but when possible, they are evaluated with both microphysical measurements and field observations. Predominant sources are combustion related, namely, fossil fuels for transportation, solid fuels for industrial and residential uses, and open burning of biomass. Total global emissions of black carbon using bottom-up inventory methods are 7500 Gg yr�-1 in the year 2000 with an uncertainty range of 2000 to 29000. However, global atmospheric absorption attributable to black carbon is too low in many models and should be increased by a factor of almost 3. After this scaling, the best estimate for the industrial-era (1750 to 2005) direct radiative forcing of atmospheric black carbon is +0.71 W m�-2 with 90% uncertainty bounds of (+0.08, +1.27)Wm�-2. Total direct forcing by all black carbon sources, without subtracting the preindustrial background, is estimated as +0.88 (+0.17, +1.48) W m�-2. Direct radiative forcing alone does not capture important rapid adjustment mechanisms. A framework is described and used for quantifying climate forcings, including rapid adjustments. The best estimate of industrial-era climate forcing of black carbon through all forcing mechanisms, including clouds and cryosphere forcing, is +1.1 W m�-2 with 90% uncertainty bounds of +0.17 to +2.1 W m�-2. Thus, there is a very high probability that black carbon emissions, independent of co-emitted species, have a positive forcing and warm the climate. We estimate that black carbon, with a total climate forcing of +1.1 W m�-2, is the second most important human emission in terms of its climate forcing in the present-day atmosphere; only carbon dioxide is estimated to have a greater forcing. Sources that emit black carbon also emit other short-lived species that may either cool or warm climate. Climate forcings from co-emitted species are estimated and used in the framework described herein. When the principal effects of short-lived co-emissions, including cooling agents such as sulfur dioxide, are included in net forcing, energy-related sources (fossil fuel and biofuel) have an industrial-era climate forcing of +0.22 (�-0.50 to +1.08) W m-�2 during the first year after emission. For a few of these sources, such as diesel engines and possibly residential biofuels, warming is strong enough that eliminating all short-lived emissions from these sources would reduce net climate forcing (i.e., produce cooling). When open burning emissions, which emit high levels of organic matter, are included in the total, the best estimate of net industrial-era climate forcing by all short-lived species from black-carbon-rich sources becomes slightly negative (�-0.06 W m�-2 with 90% uncertainty bounds of �-1.45 to +1.29 W m�-2). The uncertainties in net climate forcing from black-carbon-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both black carbon and co-emitted organic carbon. In prioritizing potential black-carbon mitigation actions, non-science factors, such as technical feasibility, costs, policy design, and implementation feasibility play important roles. The major sources of black carbon are presently in different stages with regard to the feasibility for near-term mitigation. This assessment, by evaluating the large number and complexity of the associated physical and radiative processes in black-carbon climate forcing, sets a baseline from which to improve future climate forcing estimates.

Fire in the Earth System

Fire is an important component of the Earth System that is tightly coupled with climate, vegetation, biogeochemical cycles, and human activities. Observations of how fire regimes change on seasonal to millennial timescales are providing an improved understanding of the hierarchy of controls on fire regimes. Climate is the principal control on fire regimes, although human activities have had an increasing influence on the distribution and incidence of fire in recent centuries. Understanding of the controls and variability of fire also underpins the development of models, both conceptual and numerical, that allow us to predict how future climate and land-use changes might influence fire regimes. Although fires in fire-adapted ecosystems can be important for biodiversity and ecosystem function, positive effects are being increasingly outweighed by losses of ecosystem services. As humans encroach further into the natural habitat of fire, social and economic costs are also escalating. The prospect of near-term rapid and large climate changes, and the escalating costs of large wildfires, necessitates a radical re-thinking and the development of approaches to fire management that promote the more harmonious co-existence of fire and people.

Dominant role of greenhouse-gas forcing in the recovery of Sahel rainfall

Sahelian summer rainfall, controlled by the West African monsoon, exhibited large-amplitude multidecadal variability during the twentieth century. Particularly important was the severe drought of the 1970s and 1980s, which had widespread impacts1–6. Research into the causes of this drought has identified anthropogenic aerosol forcing3,4,7 and changes in sea surface temperatures (SSTs; refs 1,2,6,8–11) as the most important drivers. Since the 1980s, there has been some recovery of Sahel rainfall amounts2–6,11–14, although not to the pre-drought levels of the 1940s and 1950s. Here we report on experiments with the atmospheric component of a state-of-the-art global climate model to identify the causes of this recovery. Our results suggest that the direct influence of higher levels of greenhouse gases in the atmosphere was the main cause, with an additional role for changes in anthropogenic aerosol precursor emissions. We find that recent changes in SSTs, although substantial, did not have a significant impact on the recovery. The simulated response to anthropogenic greenhouse-gas and aerosol forcing is consistent with a multivariate fingerprint of the observed recovery, raising confidence in our findings. Although robust predictions are not yet possible, our results suggest that the recent recovery in Sahel rainfall amounts is most likely to be sustained or amplified in the near term.

El Niño 2015/2016 impact analysis, monthly outlook November 2015

During the summer and autumn 2015, El Niño conditions in the east and central Pacific have strengthened, disrupting weather patterns throughout the tropics and into the mid-latitudes. For example, rainfall during this summer’s Indian monsoon was approximately 15% below normal. The continued strong El Niño conditions have the potential to trigger damaging impacts (e.g., droughts, famines, floods), particularly in less-developed tropical countries, which would require a swift and effective humanitarian response to mitigate damage to life and property (e.g., health, migration, infrastructure). This analysis uses key climatic variables (temperature, soil moisture and precipitation) as measures to monitor the ongoing risk of these potentially damaging impacts. The previous 2015-2016 El Niño Impact Analysis was based on observations over the past 35 years and produced Impact Tables showing the likelihood and severity of the impacts on temperature and rainfall by season. The current report is an extension of this work providing information from seasonal forecast models to give a more detailed monthly outlook of the potential near-term impacts of the current El Niño conditions by region. This information has been added to the Impact Tables in the form of a monthly outlook column. This monthly outlook is an indication of the average likely conditions for that month and region and is not a definite prediction of weather impacts.

El Niño 2015/2016 impact analysis, monthly outlook December 2015

During the summer and autumn 2015, El Niño conditions in the east and central Pacific have strengthened, disrupting weather patterns throughout the tropics and into the mid-latitudes. For example, rainfall during this summer’s Indian monsoon was approximately 15% below normal. The continued strong El Niño conditions have the potential to trigger damaging impacts (e.g. droughts, famines, floods), particularly in less-developed tropical countries, which would require a swift and effective humanitarian response to mitigate damage to life and property (e.g. health, migration, infrastructure). This analysis uses key climatic variables (temperature, soil moisture and precipitation) as measures to monitor the ongoing risk of these potentially damaging impacts. The previous 2015-2016 El Niño Impact Analysis was based on observations over the past 35 years and produced Impact Tables showing the likelihood and severity of the impacts on temperature and rainfall by season. The current report is an extension of this work providing information from observations and seasonal forecast models to give a more detailed outlook of the potential near-term impacts of the current El Niño conditions by region. This information has been added to the Impact Tables in the form of an ‘Observations and Outlook’ row. This consists of observational information for the past seasons of JJA 2015 and SON 2015, a detailed monthly outlook from 5 modeling centres for Dec 2015 and then longer-term seasonal forecast information from 2 modeling centres for the future seasons of JF 2016 and MAM 2016. The seasonal outlook information is an indication of the average likely conditions for that coming month (or season) and region and is not a definite prediction of weather impacts.

El Niño 2015/2016 impact analysis, monthly outlook January 2016

During the summer and autumn of 2015, El Niño conditions in the east and central Pacific have strengthened, disrupting weather patterns throughout the tropics and into the mid-latitudes. For example, rainfall during this summer’s Indian monsoon was approximately 15% below normal. The continued strong El Niño conditions have the potential to trigger damaging impacts (e.g., droughts, famines, floods), particularly in less-developed tropical countries, which would require a swift and effective humanitarian response to mitigate damage to life and property (e.g., health, migration, infrastructure). This analysis uses key climatic variables (temperature, soil moisture and precipitation) as measures to monitor the ongoing risk of these potentially damaging impacts. The previous 2015-2016 El Niño Impact Analysis was based on observations over the past 35 years and produced Impact Tables showing the likelihood and severity of the impacts on temperature and rainfall by season. The current report is an extension of this work providing information from observations and seasonal forecast models to give a more detailed outlook of the potential near-term impacts of the current El Niño conditions by region. This information has been added to the Impact Tables in the form of an ‘Observations and Outlook’ row. This consists of observational information for the past seasons of JJA 2015, SON 2015 and Dec 2015, a detailed monthly outlook from 4 modeling centres for Jan 2016 and then longer-term seasonal forecast information from 2 modeling centres for the future seasons of Feb 2016, MAM 2016 and Jun 2016. The seasonal outlook information is an indication of the average likely conditions for that coming month (or season) and region and is not a definite prediction of weather impacts.

El Niño 2015/2016 impact analysis, monthly outlook February 2016

During the summer and autumn of 2015, El Niño conditions in the east and central Pacific strengthened, disrupting weather patterns throughout the tropics and into the mid-latitudes. For example, rainfall during the summer’s Indian monsoon was approximately 15% below normal. The continued strong El Niño conditions have the potential to trigger damaging impacts (e.g., droughts, famines, floods), particularly in less-developed tropical countries, which would require a swift and effective humanitarian response to mitigate damage to life and property (e.g., health, migration, infrastructure). This analysis uses key climatic variables (temperature, soil moisture and precipitation) as measures to monitor the ongoing risk of these potentially damaging impacts. The previous 2015-2016 El Niño Impact Analysis was based on observations over the past 35 years and produced Impact Tables showing the likelihood and severity of the impacts on temperature and rainfall by season. The current report is an extension of this work, providing information from observations and seasonal forecast models to give a more detailed outlook of the potential near-term impacts of the current El Niño conditions by region. This information has been added to the Impact Tables in the form of an ‘Observations and Outlook’ row. This consists of observational information for the past seasons of JJA 2015, SON 2015 and DJ 2015/2016, a detailed monthly outlook from 5 modeling centres for Feb 2016 and then longer-term seasonal forecast information from 2 modeling centres for the future seasons of MAM 2016 and JJ 2016. The seasonal outlook information is an indication of the average likely conditions for that coming month (or season) and region and is not a definite prediction of weather impacts. This report has been produced by University of Reading for Evidence on Demand with the assistance of the UK Department for International Development (DFID) contracted through the Climate, Environment, Infrastructure and Livelihoods Professional Evidence and Applied Knowledge Services (CEIL PEAKS) programme, jointly managed by DAI (which incorporates HTSPE Limited) and IMC Worldwide Limited.