A framework for modeling uncertainty in regional climate change

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US

Performance Engineering in the Community Atmosphere Model

The Community Atmosphere Model (CAM) is the atmospheric component of the Community Climate System Model (CCSM) and is the primary consumer of computer resources in typical CCSM simulations. Performance engineering has been an important aspect of CAM development throughout its existence. This paper briefly summarizes these efforts and their impacts over the past five years

Downscale cascades in tracer transport test cases: an intercomparison of the dynamical cores in the Community Atmosphere Model CAM5

The accurate modeling of cascades to unresolved scales is an important part of the tracer transport component of dynamical cores of weather and climate models. This paper aims to investigate the ability of the advection schemes in the National Center for Atmospheric Research's Community Atmosphere Model version 5 (CAM5) to model this cascade. In order to quantify the effects of the different advection schemes in CAM5, four two-dimensional tracer transport test cases are presented. Three of the tests stretch the tracer below the scale of coarse resolution grids to ensure the downscale cascade of tracer variance. These results are compared with a high resolution reference solution, which is simulated on a resolution fine enough to resolve the tracer during the test. The fourth test has two separate flow cells, and is designed so that any tracer in the western hemisphere should not pass into the eastern hemisphere. This is to test whether the diffusion in transport schemes, often in the form of explicit hyper-diffusion terms or implicit through monotonic limiters, contains unphysical mixing. <br><br> An intercomparison of three of the dynamical cores of the National Center for Atmospheric Research's Community Atmosphere Model version 5 is performed. The results show that the finite-volume (CAM-FV) and spectral element (CAM-SE) dynamical cores model the downscale cascade of tracer variance better than the semi-Lagrangian transport scheme of the Eulerian spectral transform core (CAM-EUL). Each scheme tested produces unphysical mass in the eastern hemisphere of the separate cells test

An Object-Based Approach for Quantification of GCM Biases in the Simulation of Orographic Precipitation.

An object-based evaluation method to identify and quantify biases of General Circulation Models (GCMs) is introduced. The focus is on how orographic precipitation is simulated by the Eulerian Spectral Transform and the finite volume (FV) dynamical cores within the National Center of Atmospheric Research (NCAR) Community Earth System Model (CESM) with its Community Atmosphere Model (CAM). The “local” biases introduced by dynamical cores and how they evolve with varying model resolution are quantified by looking at simulated precipitation over the Coast Range and the Sierra Nevada mountains on the West Coast of North America. The first step of the object-based method involves identification of orographic precipitation features (study features) simulated differently by the CAM Eulerian Spectral Transform and CAM FV dynamical cores. We examined Atmospheric Model Intercomparison Project (AMIP) model simulations together with Global Precipitation Climatology Center (GPCC) observations to select the study features. CAM FV resembled the observed spatial pattern of precipitation better than the CAM Eulerian Spectral Transform scheme. As the second step of the method, idealized experiments were conducted running the Community Atmosphere Model (CAM) coupled with a simplified physics parameterization to understand the causes of this difference between the CAM FV and the CAM Eulerian Spectral Transform dynamical cores. Three different mechanisms of precipitation were isolated due to (a) stable upslope ascent, (b) local surface fluxes and moisture transport, and (c) resolved downstream waves. The precipitation features related to these mechanisms were isolated as “objects” using pattern recognition methods such as clustering and classification trees. The CAM Eulerian Spectral Transform model simulations become more unrealistic as the resolvable scales of the simulated precipitation gets smaller, and the amount of simulated precipitation gets larger. The reasons of this problematic representation of orographic precipitation by the CAM Eulerian Spectral Transform dynamical core (i.e. bias) can be summarized in three categories: (a) bias due to spectral filtering of the topography, (b) bias in small-scale phenomena due to spectral transform method, (c) grid scale variability (noise) due to spectral transform method. The results also indicated stronger sensitivity of the CAM Eulerian Spectral Transform dynamical core to model resolution.PhDAtmos, Oceanic & Space SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108827/1/yorgun_1.pd

A framework for modeling uncertainty in regional climate change

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.United States. Environmental Protection Agency. Climate Change Division (Cooperative Agreement #XA-83600001

The Impact of Orography and Latent Heating on the Location of the Tropical Easterly Jet

The Tropical Easterly Jet (TEJ) is a prominent atmospheric circulation feature observed during the Asian Summer Monsoon. It is generally assumed that Tibet is an essential ingredient in determining the location of the TEJ. However studies have also suggested the importance of latent heating in determining the jet location. The relative importance of Tibetan orography and latent heating is explored through simulations with a general circulation model. The simulation of TEJ by the Community Atmosphere Model, version 3.1 (CAM-3.1) has been discussed in detail. Although the simulated TEJ replicated many observed features of the jet, the jet maximum was located too far to the west when compared to observation. The precipitation in the control simulation was high to the west of India and this caused the TEJ to shift westwards by approximately the same amount. Orography was found to have minimal impact on the simulated TEJ hence indicating that latent heating is the crucial parameter. The primacy of latent heating in determining the jet location was confirmed by additional simulations where the simulated precipitation was brought closer to observations. This made the TEJ to also shift to the correct position.Comment: 14 pages including 7 figures and 1 tabl

Multiple‐instance superparameterization, part 2: the effects of stochastic convection on the simulated climate

The cloud-permitting model (CPM) of the super-parameterized Community Atmosphere Model (SP-CAM) is a stochastic parameterization. As reported in a companion paper, we have created a variant of SP-CAM, called MP-CAM, that uses the averaged feedback of ten independent two-dimensional CPMs in each global model column, in place of the single CPM of SP-CAM. This ensemble-averaged feedback is interpreted as an approximation to the feedback from a deterministic parameterization. We present evidence that MP-CAM is indeed more deterministic than SP-CAM. The climates of the SP and MP configurations are compared, giving particular attention to extreme precipitation events and convectively coupled large-scale tropical weather systems, such as the Madden-Julian Oscillation (MJO). A number of small but significant changes in the mean-state climate are uncovered, and the deterministic parameterization slightly degrades the MJO simulation

Investigating the Prediction of High Resolution Heat Waves and Extreme Precipitation and the Impact of Heat Waves on Air Quality in U.S. in the 21st Century

In this study, the perennial problem of scale is addressed with an updated set of modeling tools that include global climate, atmospheric chemistry simulation, mesoscale weather, and air quality simulations. The evaluation of coupled model performance across geographic scales and the assessment of local scale climate change impacts under a fossil fuel intensive climate change scenario Representative Concentration Pathway (RCP 8.5) was achieved by linking the global climate model Community Earth System Model (CESM), with the regional climate model Weather Research and Forecasting (WRF) Model. This study is the first evaluation of dynamical downscaling using WRF on a 4km by 4km high resolution scale in the eastern US driven by the CESM. First, the global and regional climate model results were evaluated, and an inconsistency in skin temperature during the downscaling process was corrected by modifying the land/sea mask. In comparison with observations, WRF shows statistically significant improvement over CESM in reproducing extreme weather events, with improvement for heat wave frequency estimation as high as 98%. The RCP 8.5 was used to study a possible future mid-century climate extreme in 2057-2059. Both heat waves and extreme precipitation in 2057-2059 are more severe than present climate in the Eastern US. The Northeastern US shows large increases in both heat wave intensity (3.05 ºC higher) and annual extreme precipitation (107.3 mm more per year). The implementation of a global atmospheric chemistry model (CAM-Chem) in the Community Atmosphere Model (CAM) enables the connection between the global chemistry model (CAM-Chem) and the regional chemistry model Community Multi-scale Air Quality modeling system (CMAQ). The statistical evaluation demonstrates confidence in the regional chemistry downscaling methodology. In U.S., the mean concentrations of Maximum Daily 8-hr ozone is 3.1 to 9.5 ppbv higher during the heat wave periods than non-heat wave periods in RCP 8.5, stressing the importance of control strategies during the heat wave periods

Examining chaotic convection with super-parameterization ensembles

2017 Spring.Includes bibliographical references.This study investigates a variety of features present in a new configuration of the Community Atmosphere Model (CAM) variant, SP-CAM 2.0. The new configuration (multiple-parameterization-CAM, MP-CAM) changes the manner in which the super-parameterization (SP) concept represents physical tendency feedbacks to the large-scale by using the mean of 10 independent two-dimensional cloud-permitting model (CPM) curtains in each global model column instead of the conventional single CPM curtain. The climates of the SP and MP configurations are examined to investigate any significant differences caused by the application of convective physical tendencies that are more deterministic in nature, paying particular attention to extreme precipitation events and large-scale weather systems, such as the Madden-Julian Oscillation (MJO). A number of small but significant changes in the mean state climate are uncovered, and it is found that the new formulation degrades MJO performance. Despite these deficiencies, the ensemble of possible realizations of convective states in the MP configuration allows for analysis of uncertainty in the small-scale solution, lending to examination of those weather regimes and physical mechanisms associated with strong, chaotic convection. Methods of quantifying precipitation predictability are explored, and use of the most reliable of these leads to the conclusion that poor precipitation predictability is most directly related to the proximity of the global climate model column state to atmospheric critical points. Secondarily, the predictability is tied to the availability of potential convective energy, the presence of mesoscale convective organization on the CPM grid, and the directive power of the large-scale