Climate Model Forecast Experiments for TOGA COARE

2008 ◽  
Vol 136 (3) ◽  
pp. 808-832 ◽  
Author(s):  
J. Boyle ◽  
S. Klein ◽  
G. Zhang ◽  
S. Xie ◽  
X. Wei
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Atmospheric Model ◽  
Static Stability ◽  
Geophysical Fluid Dynamics Laboratory ◽  
State Variables ◽  
Tropical Ocean ◽  
Model Version ◽  
Toga Coare

Abstract Short-term (1–10 day) forecasts are made with climate models to assess the parameterizations of the physical processes. The time period for the integrations is that of the intensive observing period (IOP) of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). The models used are the National Center for Atmospheric Research (NCAR) Community Climate Model, version 3.1 (CAM3.1); CAM3.1 with a modified deep convection parameterization; and the Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model, version 2 (AM2). The models were initialized using the state variables from the 40-yr ECMWF Re-Analysis (ERA-40). The CAM deep convective parameterization fails to demonstrate the sensitivity to the imposed forcing to simulate precipitation patterns associated with the Madden–Julian oscillations (MJOs) present during the period. AM2 and modified CAM3.1 exhibit greater correspondence to the observations at the TOGA COARE site, suggesting that convective parameterizations that have some type of limiter (as do AM2 and the modified CAM3.1) simulate the MJO rainfall with more fidelity than those without. None of the models are able to fully capture the correct phasing of westerly wind bursts with respect to precipitation in the eastward-moving MJO disturbance. Better representation of the diabatic heating and effective static stability profiles is associated with a better MJO simulation. Because the models’ errors in the forecast mode bear a resemblance to the errors in the climate mode in simulating the MJO, the forecasts may allow for a better way to dissect the reasons for model error.

EXPLORING INLAND TROPICAL CYCLONE RAINFALL AND TORNADOES UNDER FUTURE CLIMATE CONDITIONS THROUGH A CASE STUDY OF HURRICANE IVAN

2020 ◽  
Author(s):  
Dereka Carroll-Smith ◽  
Robert J. Trapp ◽  
James M. Done
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Climate Models ◽  
Climate Model ◽  
Convective Available Potential Energy ◽  
Track Length ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Climate System Model ◽  
Hurricane Ivan ◽  
Model Version

AbstractThe overarching purpose of this study is to investigate the impacts of anthropogenic climate change both on the rainfall and tornadoes associated with tropical cyclones (TCs) making landfall in the U.S. Atlantic Basin. The “pseudo-global” warming (PGW) approach is applied to Hurricane Ivan (2004), a historically prolific tropical cyclone tornado (TCT)-producing storm. Hurricane Ivan is simulated under its current climate forcings using the Weather Research and Forecasting model. This control simulation (CTRL) is then compared to PGW simulations in which the current forcings are modified by climate-change differences obtained from the Community Climate System Model version 4 (NCAR), Model for Interdisciplinary Research on Climate version 5 (MIROC), and Geophysical Fluid Dynamics Laboratory Climate Model version 3 (GFDL) climate models. Changes in TC intensity, TC rainfall, and TCT production, identified for the PGW-modified Ivan are documented and analyzed.Compared to CTRL, all three PGW simulations show an increase in TC intensity and generate substantially more accumulated rainfall over the course of Ivan’s progression overland. However, only one of the TCs under PGW (MIROC) produced more TCTs than the control. Evidence is provided that in addition to favorable environmental conditions, TCT production is related to the TC track length and to the strength of the interaction between the TC and an environmental mid-level trough. Enhanced TCT generation at landfall for MIROC and GFDL is attributed to increased values of convective available potential energy, low level shear and storm relative environmental helicity.

Metrics and Diagnostics for Precipitation-Related Processes in Climate Model Short-Range Hindcasts

2013 ◽  
Vol 26 (5) ◽  
pp. 1516-1534 ◽  
Author(s):  
H.-Y. Ma ◽  
S. Xie ◽  
J. S. Boyle ◽  
S. A. Klein ◽  
Y. Zhang
Keyword(s):  
Short Range ◽  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Regional Analysis ◽  
Atmospheric Model ◽  
Department Of Energy ◽  
Temperature Fields ◽  
Lower Probability ◽  
Model Biases

Abstract In this study, several metrics and diagnostics are proposed and implemented to systematically explore and diagnose climate model biases in short-range hindcasts and quantify how fast hindcast biases approach to climate biases with an emphasis on tropical precipitation and associated moist processes. A series of 6-day hindcasts with NCAR and the U.S. Department of Energy Community Atmosphere Model, version 4 (CAM4) and version 5 (CAM5), were performed and initialized with ECMWF operational analysis every day at 0000 UTC during the Year of Tropical Convection (YOTC). An Atmospheric Model Intercomparison Project (AMIP) type of ensemble climate simulations was also conducted for the same period. The analyses indicate that initial drifts in precipitation and associated moisture processes (“fast processes”) can be identified in the hindcasts, and the biases share great resemblance to those in the climate runs. Comparing to Tropical Rainfall Measuring Mission (TRMM) observations, model hindcasts produce too high a probability of low- to intermediate-intensity precipitation at daily time scales during northern summers, which is consistent with too frequently triggered convection by its deep convection scheme. For intense precipitation events (>25 mm day−1), however, the model produces a much lower probability partially because the model requires a much higher column relative humidity than observations to produce similar precipitation intensity as indicated by the proposed diagnostics. Regional analysis on precipitation bias in the hindcasts is also performed for two selected locations where most contemporary climate models show the same sign of bias. Based on moist static energy diagnostics, the results suggest that the biases in the moisture and temperature fields near the surface and in the lower and middle troposphere are primarily responsible for precipitation biases. These analyses demonstrate the usefulness of these metrics and diagnostics to diagnose climate model biases.

scholarly journals Dynamical Downscaling for Southeast Alaska: Historical Climate and Future Projections

2020 ◽  
Vol 59 (10) ◽  
pp. 1607-1623
Author(s):  
Rick Lader ◽  
Allison Bidlack ◽  
John E. Walsh ◽  
Uma S. Bhatt ◽  
Peter A. Bieniek
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Forest Health ◽  
Warm Season ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Southeast Alaska ◽  
Climate System Model ◽  
Model Version ◽  
Precipitation Regimes

AbstractWarming temperatures across southeast Alaska are affecting the region’s energy and transportation sectors, marine ecosystems, and forest health. More frequent above-freezing temperatures lead a transition from snow- to rain-dominant precipitation regimes, accelerating glacial mass balance loss and a leading to a greater risk for warm-season drought. Southeast Alaska has steep topographical gradients, which necessitate the use of downscaled climate information to study historical and projected periods. This study used regional dynamical downscaling at 4-km spatial resolution with the Weather Research and Forecasting Model to assess historical (1981–2010) and projected (2031–60) climate states for southeast Alaska. These simulations were driven by one reanalysis (i.e., the Climate Forecast System Reanalysis) and two climate models (i.e., the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4), which each included a historical simulation and a projected simulation. The future simulations used the representative concentration pathway 8.5 emissions scenario. Bias-corrected projections (2031–60 minus 1981–2010) indicated seasonal warming of 1°–3°C, increased precipitation during autumn (4%–12%) and winter (7%–12%), and decreased snowfall in all seasons (up to 60% in autumn). The average number of days annually with a minimum temperature below freezing dropped by more than 30. The average annual maximum consecutive 3-day precipitation amounts increased by 11%–16%, but analogous extreme snowfall amounts dropped by 5%–11%. The most substantial snow losses occurred at low-elevation and coastal locations; at many high elevations (e.g., above 1000 m), extreme snowfall amounts increased.

scholarly journals Evaluating the Uncertainty Induced by the Virtual Salt Flux Assumption in Climate Simulations and Future Projections

2010 ◽  
Vol 23 (1) ◽  
pp. 80-96 ◽  
Author(s):  
Jianjun Yin ◽  
Ronald J. Stouffer ◽  
Michael J. Spelman ◽  
Stephen M. Griffies
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Ocean Model ◽  
Salt Flux ◽  
Freshwater Flux ◽  
Unexpected Result ◽  
Geophysical Fluid Dynamics Laboratory ◽  
External Forcing ◽  
Future Evolution ◽  
Near Term

Abstract The unphysical virtual salt flux (VSF) formulation widely used in the ocean component of climate models has the potential to cause systematic and significant biases in modeling the climate system and projecting its future evolution. Here a freshwater flux (FWF) and a virtual salt flux version of the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (GFDL CM2.1) are used to evaluate and quantify the uncertainties induced by the VSF formulation. Both unforced and forced runs with the two model versions are performed and compared in detail. It is found that the differences between the two versions are generally small or statistically insignificant in the unforced control runs and in the runs with a small external forcing. In response to a large external forcing, however, some biases in the VSF version become significant, especially the responses of regional salinity and global sea level. However, many fundamental aspects of the responses differ only quantitatively between the two versions. An unexpected result is the distinctly different ENSO responses. Under a strong external freshwater forcing, the great enhancement of the ENSO variability simulated by the FWF version does not occur in the VSF version and is caused by the overexpansion of the top model layer. In summary, the principle assumption behind using virtual salt flux is not seriously violated and the VSF model has the ability to simulate the current climate and project near-term climate evolution. For some special studies such as a large hosing experiment, however, both the VSF formulation and the use of the FWF in the geopotential coordinate ocean model could have some deficiencies and one should be cautious to avoid them.

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

Influence of Ocean and Atmosphere Components on Simulated Climate Sensitivities

2013 ◽  
Vol 26 (1) ◽  
pp. 231-245 ◽  
Author(s):  
Michael Winton ◽  
Alistair Adcroft ◽  
Stephen M. Griffies ◽  
Robert W. Hallberg ◽  
Larry W. Horowitz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Coupled Model ◽  
Ocean Model ◽  
Historical Simulation ◽  
Model Version ◽  
Salinity Response ◽  
The Impact

Abstract The influence of alternative ocean and atmosphere subcomponents on climate model simulation of transient sensitivities is examined by comparing three GFDL climate models used for phase 5 of the Coupled Model Intercomparison Project (CMIP5). The base model ESM2M is closely related to GFDL’s CMIP3 climate model version 2.1 (CM2.1), and makes use of a depth coordinate ocean component. The second model, ESM2G, is identical to ESM2M but makes use of an isopycnal coordinate ocean model. The authors compare the impact of this “ocean swap” with an “atmosphere swap” that produces the GFDL Climate Model version 3 (CM3) by replacing the AM2 atmospheric component with AM3 while retaining a depth coordinate ocean model. The atmosphere swap is found to have much larger influence on sensitivities of global surface temperature and Northern Hemisphere sea ice cover. The atmosphere swap also introduces a multidecadal response time scale through its indirect influence on heat uptake. Despite significant differences in their interior ocean mean states, the ESM2M and ESM2G simulations of these metrics of climate change are very similar, except for an enhanced high-latitude salinity response accompanied by temporarily advancing sea ice in ESM2G. In the ESM2G historical simulation this behavior results in the establishment of a strong halocline in the subpolar North Atlantic during the early twentieth century and an associated cooling, which are counter to observations in that region. The Atlantic meridional overturning declines comparably in all three models.

scholarly journals GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales

2018 ◽  
Vol 115 (18) ◽  
pp. 4577-4582 ◽  
Author(s):  
Kathleen A. Schiro ◽  
Fiaz Ahmed ◽  
Scott E. Giangrande ◽  
J. David Neelin
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Potential Temperature ◽  
Strong Relationship ◽  
Field Campaign ◽  
Convective Systems ◽  
Tropical Oceans ◽  
Mesoscale Convective ◽  
Environmental Air

A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multiinstrument evidence from the Green Ocean Amazon field campaign (2014–2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deep-inflow mixing yields a strong precipitation–buoyancy relation across the tropics. Deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models.

scholarly journals Comparison of warming trends over the last century around Antarctica from three coupled models

1998 ◽  
Vol 27 ◽  
pp. 565-570 ◽  
Author(s):  
William M. Connolley ◽  
Siobhan P. O'Farrell
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Large Temperature ◽  
Coupled Models ◽  
Temperature Changes ◽  
Model Variability ◽  
The Antarctic

We compare observed temperature variations in Antarctica with climate-model runs over the last century. The models used are three coupled global climate models (GCMs) — the UKMO, the CSIRO and the MPI forced by the CO2 increases observed over the last century, and an atmospheric model experiment forced with observed sea-surface temperatures and sea-ice extents over the last century. Despite some regions of agreement, in general the GCM runs appear to be incompatible with each other and with the observations, although the short observational record and high natural variability make verification difficult. One of the best places for a more detailed study is the Antarctic Peninsula where the density of stations is higher and station records are longer than elsewhere in Antarctica. Observations show that this area has seen larger temperature rises than anywhere else in Antarctica. None of the three GCMs simulate such large temperature changes in the Peninsula region, in either climate-change runs radiatively forced by CO2 increases or control runs which assess the level of model variability.

Dust Aerosol Important for Snowball Earth Deglaciation

2010 ◽  
Vol 23 (15) ◽  
pp. 4121-4132 ◽  
Author(s):  
Dorian S. Abbot ◽  
Itay Halevy
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Atmospheric Model ◽  
Dust Aerosol ◽  
Snowball Earth ◽  
Dust Aerosols ◽  
Co2 Levels ◽  
The Impact

Abstract Most previous global climate model simulations could only produce the termination of Snowball Earth episodes at CO2 partial pressures of several tenths of a bar, which is roughly an order of magnitude higher than recent estimates of CO2 levels during and shortly after Snowball events. These simulations have neglected the impact of dust aerosols on radiative transfer, which is an assumption of potentially grave importance. In this paper it is argued, using the Dust Entrainment and Deposition (DEAD) box model driven by GCM results, that atmospheric dust aerosol concentrations may have been one to two orders of magnitude higher during a Snowball Earth event than today. It is furthermore asserted on the basis of calculations using NCAR’s Single Column Atmospheric Model (SCAM)—a radiative–convective model with sophisticated aerosol, cloud, and radiative parameterizations—that when the surface albedo is high, such increases in dust aerosol loading can produce several times more surface warming than an increase in the partial pressure of CO2 from 10−4 to 10−1 bar. Therefore the conclusion is reached that including dust aerosols in simulations may reconcile the CO2 levels required for Snowball termination in climate models with observations.

scholarly journals Effect of prescribed sea surface conditions on the modern and future Antarctic surface climate simulated by the ARPEGE atmosphere general circulation model

2019 ◽  
Vol 13 (11) ◽  
pp. 3023-3043
Author(s):  
Julien Beaumet ◽  
Michel Déqué ◽  
Gerhard Krinner ◽  
Cécile Agosta ◽  
Antoinette Alias
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Coupled Climate Model ◽  
Surface Climate ◽  
Late 21St Century ◽  
The Antarctic

Abstract. Owing to increase in snowfall, the Antarctic Ice Sheet surface mass balance is expected to increase by the end of the current century. Assuming no associated response of ice dynamics, this will be a negative contribution to sea-level rise. However, the assessment of these changes using dynamical downscaling of coupled climate model projections still bears considerable uncertainties due to poorly represented high-southern-latitude atmospheric circulation and sea surface conditions (SSCs), that is sea surface temperature and sea ice concentration. This study evaluates the Antarctic surface climate simulated using a global high-resolution atmospheric model and assesses the effects on the simulated Antarctic surface climate of two different SSC data sets obtained from two coupled climate model projections. The two coupled models from which SSCs are taken, MIROC-ESM and NorESM1-M, simulate future Antarctic sea ice trends at the opposite ends of the CMIP5 RCP8.5 projection range. The atmospheric model ARPEGE is used with a stretched grid configuration in order to achieve an average horizontal resolution of 35 km over Antarctica. Over the 1981–2010 period, ARPEGE is driven by the SSCs from MIROC-ESM, NorESM1-M and CMIP5 historical runs and by observed SSCs. These three simulations are evaluated against the ERA-Interim reanalyses for atmospheric general circulation as well as the MAR regional climate model and in situ observations for surface climate. For the late 21st century, SSCs from the same coupled climate models forced by the RCP8.5 emission scenario are used both directly and bias-corrected with an anomaly method which consists in adding the future climate anomaly from coupled model projections to the observed SSCs with taking into account the quantile distribution of these anomalies. We evaluate the effects of driving the atmospheric model by the bias-corrected instead of the original SSCs. For the simulation using SSCs from NorESM1-M, no significantly different climate change signals over Antarctica as a whole are found when bias-corrected SSCs are used. For the simulation driven by MIROC-ESM SSCs, a significant additional increase in precipitation and in winter temperatures for the Antarctic Ice Sheet is obtained when using bias-corrected SSCs. For the range of Antarctic warming found (+3 to +4 K), we confirm that snowfall increase will largely outweigh increases in melt and rainfall. Using the end members of sea ice trends from the CMIP5 RCP8.5 projections, the difference in warming obtained (∼ 1 K) is much smaller than the spread of the CMIP5 Antarctic warming projections. This confirms that the errors in representing the Southern Hemisphere atmospheric circulation in climate models are also determinant for the diversity of their projected late 21st century Antarctic climate change.

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