Bistability of the Atlantic overturning circulation in a global climate model and links to ocean freshwater transport

The possibility of a rapid collapse in the strength of the Atlantic meridional overturning circulation (AMOC), with associated impacts on climate, has long been recognized. The suggested basis for this risk is the existence of two stable regimes of the AMOC (‘on’ and ‘off’), and such bistable behaviour has been identified in a range of simplified climate models. However, up to now, no state-of-the-art atmosphere-ocean coupled global climate model (AOGCM) has exhibited such behaviour, leading to the interpretation that the AMOC is more stable than simpler models indicate. Here we demonstrate AMOC bistability in the response to freshwater perturbations in the FAMOUS AOGCM - the most complex AOGCM to exhibit such behaviour to date. The results also support recent suggestions that the direction of the net freshwater transport at the southern boundary of the Atlantic by the AMOC may be a useful physical indicator of the existence of bistability. We also present new estimates for this net freshwater transport by the AMOC from a range of ocean reanalyses which suggest that the Atlantic AMOC is currently in a bistable regime, although with large uncertainties. More accurate observational constraints, and an improved physical understanding of this quantity, could help narrow uncertainty in the future evolution of the AMOC and to assess the risk of a rapid AMOC collapse.

Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps

An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable-resolution global model are considered, all with a grid spacing of 50 km. The 15-year integrations were forced from reanalyses and observed sea surface temperature and sea ice (global model from sea surface only). The observational reference is based on 6400 rain gauge records (10–50 stations per grid box). Evaluation statistics encompass mean precipitation, wet-day frequency, precipitation intensity, and quantiles of the frequency distribution. For mean precipitation, the models reproduce the characteristics of the annual cycle and the spatial distribution. The domain mean bias varies between −23% and +3% in winter and between −27% and −5% in summer. Larger errors are found for other statistics. In summer, all models underestimate precipitation intensity (by 16–42%) and there is a too low frequency of heavy events. This bias reflects too dry summer mean conditions in three of the models, while it is partly compensated by too many low-intensity events in the other two models. Similar intermodel differences are found for other European subregions. Interestingly, the model errors are very similar between the two models with the same dynamical core (but different parameterizations) and they differ considerably between the two models with similar parameterizations (but different dynamics). Despite considerable biases, the models reproduce prominent mesoscale features of heavy precipitation, which is a promising result for their use in climate change downscaling over complex topography.

High-resolution global climate simulations with the ECMWF model in Project Athena: experimental design, model climate, and seasonal forecast skill

The sensitivity to the horizontal resolution of the climate, anthropogenic climate change, and seasonal predictive skill of the ECMWF model has been studied as part of Project Athena—an international collaboration formed to test the hypothesis that substantial progress in simulating and predicting climate can be achieved if mesoscale and subsynoptic atmospheric phenomena are more realistically represented in climate models. In this study the experiments carried out with the ECMWF model (atmosphere only) are described in detail. Here, the focus is on the tropics and the Northern Hemisphere extratropics during boreal winter. The resolutions considered in Project Athena for the ECMWF model are T159 (126 km), T511 (39 km), T1279 (16 km), and T2047 (10 km). It was found that increasing horizontal resolution improves the tropical precipitation, the tropical atmospheric circulation, the frequency of occurrence of Euro-Atlantic blocking, and the representation of extratropical cyclones in large parts of the Northern Hemisphere extratropics. All of these improvements come from the increase in resolution from T159 to T511 with relatively small changes for further resolution increases to T1279 and T2047, although it should be noted that results from this very highest resolution are from a previously untested model version. Problems in simulating the Madden–Julian oscillation remain unchanged for all resolutions tested. There is some evidence that increasing horizontal resolution to T1279 leads to moderate increases in seasonal forecast skill during boreal winter in the tropics and Northern Hemisphere extratropics. Sensitivity experiments are discussed, which helps to foster a better understanding of some of the resolution dependence found for the ECMWF model in Project Athena

The METAFOR project: preserving data through metadata standards for climate models and simulations

Climate modeling is a complex process, requiring accurate and complete metadata in order to identify, assess and use climate data stored in digital repositories. The preservation of such data is increasingly important given the development of ever-increasingly complex models to predict the effects of global climate change. The EU METAFOR project has developed a Common Information Model (CIM) to describe climate data and the models and modelling environments that produce this data. There is a wide degree of variability between different climate models and modelling groups. To accommodate this, the CIM has been designed to be highly generic and flexible, with extensibility built in. METAFOR describes the climate modelling process simply as "an activity undertaken using software on computers to produce data." This process has been described as separate UML packages (and, ultimately, XML schemas). This fairly generic structure canbe paired with more specific "controlled vocabularies" in order to restrict the range of valid CIM instances. The CIM will aid digital preservation of climate models as it will provide an accepted standard structure for the model metadata. Tools to write and manage CIM instances, and to allow convenient and powerful searches of CIM databases,. Are also under development. Community buy-in of the CIM has been achieved through a continual process of consultation with the climate modelling community, and through the METAFOR team’s development of a questionnaire that will be used to collect the metadata for the Intergovernmental Panel on Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) model runs.

Integration of Space and In Situ Observations to Study Global Climate Change

The currently available model-based global data sets of atmospheric circulation are a by-product of the daily requirement of producing initial conditions for numerical weather prediction (NWP) models. These data sets have been quite useful for studying fundamental dynamical and physical processes, and for describing the nature of the general circulation of the atmosphere. However, due to limitations in the early data assimilation systems and inconsistencies caused by numerous model changes, the available model-based global data sets may not be suitable for studying global climate change. A comprehensive analysis of global observations based on a four-dimensional data assimilation system with a realistic physical model should be undertaken to integrate space and in situ observations to produce internally consistent, homogeneous, multivariate data sets for the earth's climate system. The concept is equally applicable for producing data sets for the atmosphere, the oceans, and the biosphere, and such data sets will be quite useful for studying global climate change.

A comprehensive benchmarking system for evaluating global vegetation models.

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover, composition and 5 height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, 10 and are compared to scores based on the temporal or spatial mean value of the observations and a “random” model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), and the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global 15 vegetation models (DGVMs). SDBM reproduces observed CO2 seasonal cycles, but its simulation of independent measurements of net primary production (NPP) is too high. The two DGVMs show little difference for most benchmarks (including the interannual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified 20 several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change 25 impacts and feedbacks.

The influence of anthropogenic aerosol on multi-decadal variations of historical global climate

Analysis of single forcing runs from CMIP5 (the fifth Coupled Model Intercomparison Project) simulations shows that the mid-twentieth century temperature hiatus, and the coincident decrease in precipitation, is likely to have been influenced strongly by anthropogenic aerosol forcing. Models that include a representation of the indirect effect of aerosol better reproduce inter-decadal variability in historical global-mean near-surface temperatures, particularly the cooling in the 1950s and 1960s, compared to models with representation of the aerosol direct effect only. Models with the indirect effect also show a more pronounced decrease in precipitation during this period, which is in better agreement with observations, and greater inter-decadal variability in the inter-hemispheric temperature difference. This study demonstrates the importance of representing aerosols, and their indirect effects, in general circulation models, and suggests that inter-model diversity in aerosol burden and representation of aerosol–cloud interaction can produce substantial variation in simulations of climate variability on multi decadal timescales.

A global assessment of the impact of climate change on water scarcity

This paper presents a global scale assessment of the impact of climate change on water scarcity. Patterns of climate change from 21 Global Climate Models (GCMs) under four SRES scenarios are applied to a global hydrological model to estimate water resources across 1339 watersheds. The Water Crowding Index (WCI) and the Water Stress Index (WSI) are used to calculate exposure to increases and decreases in global water scarcity due to climate change. 1.6 (WCI) and 2.4 (WSI) billion people are estimated to be currently living within watersheds exposed to water scarcity. Using the WCI, by 2050 under the A1B scenario, 0.5 to 3.1 billion people are exposed to an increase in water scarcity due to climate change (range across 21 GCMs). This represents a higher upper-estimate than previous assessments because scenarios are constructed from a wider range of GCMs. A substantial proportion of the uncertainty in the global-scale effect of climate change on water scarcity is due to uncertainty in the estimates for South Asia and East Asia. Sensitivity to the WCI and WSI thresholds that define water scarcity can be comparable to the sensitivity to climate change pattern. More of the world will see an increase in exposure to water scarcity than a decrease due to climate change but this is not consistent across all climate change patterns. Additionally, investigation of the effects of a set of prescribed global mean temperature change scenarios show rapid increases in water scarcity due to climate change across many regions of the globe, up to 2°C, followed by stabilisation to 4°C.

Urban climates and global climate change

Cities and global climate change are closely linked: cities are where the bulk of greenhouse gas emissions take place through the consumption of fossil fuels; they are where an increasing proportion of the world’s people live; and they also generate their own climate – commonly characterized by the urban heat island. In this way, understanding the way cities affect the cycling of energy, water, and carbon to create an urban climate is a key element of climate mitigation and adaptation strategies, especially in the context of rising global temperatures and deteriorating air quality in many cities. As climate models resolve finer spatial-scales, they will need to represent those areas in which more than 50% of the world’s population already live to provide climate projections that are of greater use to planning and decision-making. Finally, many of the processes that are instrumental in determining urban climate are the same factors leading to global anthropogenic climate change, namely regional-scale land-use changes; increased energy use; and increased emissions of climatically-relevant atmospheric constituents. Cities are therefore both a case study for understanding, and an agent in mitigating, anthropogenic climate change. This chapter reviews and summarizes the current state of understanding of the physical basis of urban climates, as well as our ability to represent these in models. We argue that addressing the challenges of managing urban environments in a changing climate requires understanding the energy, water, and carbon balances for an urban landscape and, importantly, their interactions and feedbacks, together with their links to human behaviour and controls. We conclude with some suggestions for where further research is needed.

A comprehensive benchmarking system for evaluating global vegetation models

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.

Comparing tropical precipitation simulated by the Met Office NWP and climate models with satellite observations

Forecasts of precipitation and water vapor made by the Met Office global numerical weather prediction (NWP) model are evaluated using products from satellite observations by the Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) for June–September 2011, with a focus on tropical areas (308S–308N). Consistent with previous studies, the predicted diurnal cycle of precipitation peaks too early (by ;3 h) and the amplitude is too strong over both tropical ocean and land regions. Most of the wet and dry precipitation biases, particularly those over land, can be explained by the diurnal-cycle discrepancies. An overall wet bias over the equatorial Pacific and Indian Oceans and a dry bias over the western Pacific warmpool and India are linked with similar biases in the climate model, which shares common parameterizations with the NWP version. Whereas precipitation biases develop within hours in the NWP model, underestimates in water vapor (which are assimilated by the NWP model) evolve over the first few days of the forecast. The NWP simulations are able to capture observed daily-to-intraseasonal variability in water vapor and precipitation, including fluctuations associated with tropical cyclones.

The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models

Experiments with CO2 instantaneously quadrupled and then held constant are used to show that the relationship between the global-mean net heat input to the climate system and the global-mean surface-air-temperature change is nonlinear in Coupled Model Intercomparison Project phase 5 (CMIP5) Atmosphere-Ocean General Circulation Models (AOGCMs). The nonlinearity is shown to arise from a change in strength of climate feedbacks driven by an evolving pattern of surface warming. In 23 out of the 27 AOGCMs examined the climate feedback parameter becomes significantly (95% confidence) less negative – i.e. the effective climate sensitivity increases – as time passes. Cloud feedback parameters show the largest changes. In the AOGCM-mean approximately 60% of the change in feedback parameter comes from the topics (30N-30S). An important region involved is the tropical Pacific where the surface warming intensifies in the east after a few decades. The dependence of climate feedbacks on an evolving pattern of surface warming is confirmed using the HadGEM2 and HadCM3 atmosphere GCMs (AGCMs). With monthly evolving sea-surface-temperatures and sea-ice prescribed from its AOGCM counterpart each AGCM reproduces the time-varying feedbacks, but when a fixed pattern of warming is prescribed the radiative response is linear with global temperature change or nearly so. We also demonstrate that the regression and fixed-SST methods for evaluating effective radiative forcing are in principle different, because rapid SST adjustment when CO2 is changed can produce a pattern of surface temperature change with zero global mean but non-zero change in net radiation at the top of the atmosphere (~ -0.5 Wm-2 in HadCM3).

Climate models without pre-industrial volcanic forcing underestimate historical ocean thermal expansion

Episodic explosive volcanic eruptions are a natural part of the climate system but are often omitted from atmosphere-ocean general circulation model (AOGCM) preindustrial spin-up and control experiments. This omission imposes a negative bias on ocean heat uptake in simulations of the historical period. In models of a range of complexity, we find that global-mean sea level rise due to thermal expansion during the last ∼ 150 years is consequently underestimated by 5–30 mm, which is a substantial proportion of the model mean of 50 mm in Coupled Model Intercomparison Project Phase 3 AOGCMs with anthropogenic forcing only, and is therefore important in accounting for 20th century sea level rise. We test and recommend a procedure for removing the bias.

Characteristics of tropical cyclones in high-resolution models in the present climate

The global characteristics of tropical cyclones (TCs) simulated by several climate models are analyzed and compared with observations. The global climate models were forced by the same sea surface temperature (SST) fields in two types of experiments, using climatological SST and interannually varying SST. TC tracks and intensities are derived from each model's output fields by the group who ran that model, using their own preferred tracking scheme; the study considers the combination of model and tracking scheme as a single modeling system, and compares the properties derived from the different systems. Overall, the observed geographic distribution of global TC frequency was reasonably well reproduced. As expected, with the exception of one model, intensities of the simulated TC were lower than in observations, to a degree that varies considerably across models.