Earth Systems

Changing the Climate Sensitivity of an Atmospheric General Circulation Model through Cloud Radiative Adjustment

Conducting probabilistic climate projections with a particular climate model requires the ability to vary the model's characteristics, such as its climate sensitivity. In this study, the authors implement and validate a method to change the climate sensitivity of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), through cloud radiative adjustment. Results show that the cloud radiative adjustment method does not lead to physically unrealistic changes in the model's response to an external forcing, such as doubling CO2 concentrations or increasing sulfate aerosol concentrations. Furthermore, this method has some advantages compared to the traditional perturbed physics approach. In particular, the cloud radiative adjustment method can produce any value of climate sensitivity within the wide range of uncertainty based on the observed twentieth century climate change. As a consequence, this method allows Monte Carlo-type probabilistic climate forecasts to be conducted where values of uncertain parameters not only cover the whole uncertainty range, but cover it homogeneously. Unlike the perturbed physics approach that can produce several versions of a model with the same climate sensitivity but with very different regional patterns of change, the cloud radiative adjustment method can only produce one version of the model with a specific climate sensitivity. As such, a limitation of this method is that it cannot cover the full uncertainty in regional patterns of climate change.

Climate change impacts on extreme events in the United States: an uncertainty analysis

In this study, we analyze changes in extreme temperature and precipitation over the US in a 60-member ensemble simulation of the 21st century with the Massachusetts Institute of Technology (MIT) Integrated Global System Model–Community Atmosphere Model (IGSM-CAM). Four values of climate sensitivity, three emissions scenarios and five initial conditions are considered. The results show a general intensification and an increase in the frequency of extreme hot temperatures and extreme precipitation events over most of the US. Extreme cold temperatures are projected to decrease in intensity and frequency, especially over the northern parts of the US. This study displays a wide range of future changes in extreme events in the US, even simulated by a single climate model. Results clearly show that the choice of policy is the largest source of uncertainty in the magnitude of the changes. The impact of the climate sensitivity is largest for the unconstrained emissions scenario and the implementation of a stabilization scenario drastically reduces the changes in extremes, even for the highest climate sensitivity considered. Finally, simulations with different initial conditions show conspicuously different patterns and magnitudes of changes in extreme events, underlining the role of natural variability in projections of changes in extreme events.

Electronic supplementary material

The online version of this journal article (doi:10.1007/s10584-013-1048-1) contains supplementary material, which is available to authorized users.

This article is part of a Special Issue on “A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States” edited by Jeremy Martinich, John Reilly, Stephanie Waldhoff, Marcus Sarofim, and James McFarland.

Climate Change Impacts on Extreme Events in the United States: An Uncertainty Analysis

Extreme weather and climate events, such as heat waves, droughts and severe precipitation events, have substantial impacts on ecosystems and the economy. However, future climate simulations display large uncertainty in mean changes. As a result, the uncertainty in future changes of extreme events, especially at the local and national level, is large. In this study, we analyze changes in extreme events over the US in a 60-member ensemble simulation of the 21st century with the Massachusetts Institute of Technology (MIT) Integrated Global System Model–Community Atmosphere Model (IGSM-CAM). Four values of climate sensitivity, three emissions scenarios and five initial conditions are considered. The results show a general intensification of extreme daily maximum temperatures and extreme precipitation events over most of the US. The number of rain days per year increases over the Great Plains but decreases in the northern Pacific Coast and along the Gulf Coast. Extreme daily minimum temperatures increase, especially over the northern parts of the US. As a result, the number of frost days per year decreases over the entire US and the frost-free zone expands northward. This study displays a wide range of future changes in extreme events in the US, even simulated by a single climate model. Nonetheless, it clearly shows that under a reference emissions scenario with no climate policy, changes in extreme events reach dangerous levels, especially for large values of climate sensitivity. On the other hand, the implementation of a stabilization scenario drastically reduces the changes in extremes, even for the highest climate sensitivity considered.

Climate impacts of a large-scale biofuels expansion

A global biofuels program will potentially lead to intense pressures on land supply and cause widespread transformations in land use. These transformations can alter the Earth climate system by increasing greenhouse gas (GHG) emissions from land use changes and by changing the reflective and energy exchange characteristics of land ecosystems. Using an integrated assessment model that links an economic model with climate, terrestrial biogeochemistry, and biogeophysics models, we examined the biogeochemical and biogeophysical effects of possible land use changes from an expanded global second-generation bioenergy program on surface temperatures over the first half of the 21st century. Our integrated assessment model shows that land clearing, especially forest clearing, has two concurrent effects—increased GHG emissions, resulting in surface air warming; and large changes in the land’s reflective and energy exchange characteristics, resulting in surface air warming in the tropics but cooling in temperate and polar regions. Overall, these biogeochemical and biogeophysical effects will only have a small impact on global mean surface temperature. However, the model projects regional patterns of enhanced surface air warming in the Amazon Basin and the eastern part of the Congo Basin. Therefore, global land use strategies that protect tropical forests could dramatically reduce air warming projected in these regions.

Climatology and trends in the forcing of the stratospheric ozone transport

A thorough analysis of the ozone transport was carried out using the Transformed-Mean Eulerian (TEM) tracer continuity equation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). In this budget analysis, the chemical net production term, which is calculated as the residual of the other terms, displays the correct features of a chemical sink and source term, including location and seasonality, and shows good agreement in magnitude compared to other methods of calculating ozone loss rates. This study provides further insight into the role of the eddy ozone transport and underlines its fundamental role in the recovery of the ozone hole during spring. The trend analysis reveals that the ozone hole intensification over the 1980–2001 period is not solely related to the trend in chemical losses, but more specifically to the balance between the trends in chemical losses and ozone transport. That is because, in the Southern Hemisphere from October to December, the large increase in the chemical destruction of ozone is balanced by an equally large trend in the eddy transport, associated with a small increase in the mean transport. This study shows that the increase in the eddy transport is characterized by more poleward ozone eddy flux by transient waves in the midlatitudes and by stationary waves in the polar region. Overall, this study makes clearer the close interaction between the trends in ozone chemistry and ozone transport. It reveals that the eddy ozone transport and its long-term changes are an important natural mitigation mechanism for the ozone hole. This work also underlines the need for diagnostics of the eddy transport in chemical transport models used to investigate future ozone recovery.

Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF

In this study, the Weather Research and Forecasting Model (WRF) is coupled with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF are simple and lack the capability to simulate carbon dioxide (for example, the popular NOAH LSM). ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically.

Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF-ACASA and WRF-NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy, it also properly accounts for the dominant biological and physical processes describing ecosystem-atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impacts of various land surface and model components on atmospheric and surface conditions.

Examination of Climate Response to Historical and Projected Forcings over Years 850-4000 with the MIT IGSM

The MIT IGSM is used for a study of the climate response to various historical and projected forcings over the period 850-4000 AD. The MIT IGSM includes a zonally-averaged atmospheric model coupled to land and ocean models. Both land and ocean models simulate carbon cycle. Two configurations of the IGSM were used in the simulations; one with the MIT 3D OGCM and other with anomaly diffusing ocean model. Over the period 850-2005, a historical run with all time-varying natural and anthropogenic forcings is compared to a set of runs where only a single component of the forcing time series is varied. Over 2005-3000, climate projections as forced by four different Representation Concentration Pathways are compared. These projections are extended by decreasing forcings back to pre-industrial levels over years 3000-4000. In addition to changes in surface air temperature, carbon uptake in the ocean and land systems and changes in the oceans’ large-scale circulation are a focus in analyses of these simulations. Simulations with interactive carbon cycle and prescribed carbon emissions were also carried out. Dependency of the projected changes on assumptions about climate system parameters, such as climate sensitivity, rate of oceanic heat uptake and aerosol forcing were studied using the IGSM with simplified ocean model.

Exploring the Terrestrial Ecosystem Response to Extreme Weather Events using Multiple Land Surface Model

This study investigates the complex terrestrial ecosystems response to extreme weather events using three different land surface models. Previous studies have showed that extreme weather events can have serious and damaging impacts on human and natural systems and they are most evident on regional and local scales. Under climate change, extreme weather events are likely to increase in both magnitude and frequency, making realistic simulation of ecosystems response to extreme events more essential than ever in assessing the potential damaging impacts. Three different land surface models are used to explore the impacts of extreme events on regional to continental ecosystem responses. The Terrestrial Ecosystem Model (TEM) is a process-based ecosystem model that uses spatially referenced information on climate, elevation, soils, vegetation and water availability to make monthly estimates of vegetation and soil carbon and nitrogen fluxes and pool sizes. The Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) is a multi-layered land surface model based on eddy-covariance theory to calculate the biosphere-atmosphere exchanges of carbon dioxide, water, and momentums. The Community Land Model (CLM) is a community-based model widely used in global-scale land data assimilation research. The study focuses on the complex interactions and feedbacks between the terrestrial ecosystem and the atmosphere such as water cycle, carbon and nitrogen budgets, and environmental conditions. The model simulations and performances are evaluated using the biogeophysical and micrometeorological observation data from the AmeriFlux sites across the continental US. This study compares and evaluates the ability of different models and their key components to capture terrestrial response to extreme weather events.

Historical and idealized model experiments: an intercomparison of Earth system models of intermediate complexity

Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth’s orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2× and 4×CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that landuse emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions.

How important is diversity for capturing environmental-change responses in ecosystem models?

Marine ecosystem models used to investigate how global change affects ocean ecosystems and their functioning typically omit pelagic plankton diversity. Diversity, however, may affect functions such as primary production and their sensitivity to environmental changes. Here we use a global ocean ecosystem model that explicitly resolves phytoplankton diversity by defining subtypes within four phytoplankton functional types (PFTs). We investigate the model's ability to capture diversity effects on primary production under environmental change. An idealized scenario with a sudden reduction in vertical mixing causes diversity and primary-production changes that turn out to be largely independent of the number of coexisting phytoplankton subtypes. The way diversity is represented in the model provides a small number of niches with respect to nutrient use in accordance with the PFTs defined in the model. Increasing the number of phytoplankton subtypes increases the resolution within the niches. Diversity effects such as niche complementarity operate between, but not within PFTs, and are constrained by the variety of traits and trade-offs resolved in the model. The number and nature of the niches formulated in the model, for example via trade-offs or different PFTs, thus determines the diversity effects on ecosystem functioning captured in ocean ecosystem models.

MIT Global Change

Earth Systems

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