scholarly journals The Impact of Regional Multidecadal and Century-Scale Internal Climate Variability on Sea Level Trends in CMIP5 Models

2015 ◽  
Vol 28 (2) ◽  
pp. 853-861 ◽  
Author(s):  
Mark Carson ◽  
Armin Köhl ◽  
Detlef Stammer
Keyword(s):  
Sea Level ◽  
Time Scales ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Level Control ◽  
Ensemble Spread ◽  
Tropical Ocean ◽  
Regional Sea Level ◽  
The Impact

Abstract Regional sea surface height variability due to internal climate fluctuations is estimated using preindustrial control runs of 21 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Projected sea level trends of the representative concentration pathway 4.5 (RCP4.5) scenario for 20-, 50-, and 100-yr intervals grow from being largely dominated by internal variability on shorter time scales to being the dominant sea level signal on long time scales. The internal variability is estimated by calculating overlapping trends for the various time scales on the regional sea level control run output from each model. When compared to the ensemble spread of the RCP4.5 scenario trends, the internal variability remains a substantial portion of the spread even after 50 years. The regional ensemble mean trends are mostly larger than the ensemble spread for the 50-yr interval and are larger everywhere, except for part of the central Arctic and the Southern Ocean for the 100-yr projection. Although it is unclear whether the model internal variability estimate will be comparable to long-term variability in the real ocean, the authors compare the strength of the estimate to satellite altimetry and find that altimetry-based trends may be larger in tropical ocean regions, with only limited extratropical regions rising above the internal variability. The authors also analyze a single model’s internal variability against its future RCP4.5-projected sea level and show that, by 50 years, many regional sea level trends are larger than the underlying internal variability, though this variability still accounts for more than a third of the trend magnitude for almost half of the extratropical ocean.

scholarly journals Oceanic Stochastic Parameterizations in a Seasonal Forecast System

2016 ◽  
Vol 144 (5) ◽  
pp. 1867-1875 ◽  
Author(s):  
M. Andrejczuk ◽  
F. C. Cooper ◽  
S. Juricke ◽  
T. N. Palmer ◽  
A. Weisheimer ◽  
...  
Keyword(s):  
Time Scales ◽  
Forecast Error ◽  
Coupled Model ◽  
Active Regions ◽  
Coupled System ◽  
Western Boundary ◽  
Ensemble Spread ◽  
Ensemble Forecasts ◽  
Boundary Currents ◽  
The Impact

Stochastic parameterization provides a methodology for representing model uncertainty in ensemble forecasts. Here the impact on forecast reliability over seasonal time scales of three existing stochastic parameterizations in the ocean component of a coupled model is studied. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the stochastically perturbed parameterization tendency (SPPT) scheme, which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely, the western boundary currents and the Southern Ocean where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error on seasonal time scales. While there are good grounds for implementing stochastic schemes in ocean models, the results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.

scholarly journals Reply to “Comment on ‘The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity’”

2020 ◽  
Vol 33 (1) ◽  
pp. 397-404 ◽  
Author(s):  
Nicholas Lewis ◽  
Judith Curry
Keyword(s):  
Time Windows ◽  
Internal Variability ◽  
Historical Record ◽  
Cmip5 Models ◽  
Temperature Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Total Uncertainty ◽  
The Impact ◽  
The Relationship

AbstractCowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust—in particular against sea surface temperature bias correction uncertainty—than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus-length windows is closely in line with that estimated using the LC18 windows and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that, when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate but fluctuates as a result of multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.

scholarly journals Climate Drift in the CMIP5 Models*

2013 ◽  
Vol 26 (21) ◽  
pp. 8597-8615 ◽  
Author(s):  
Alexander Sen Gupta ◽  
Nicolas C. Jourdain ◽  
Jaclyn N. Brown ◽  
Didier Monselesan
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Deep Ocean ◽  
Internal Variability ◽  
Correction Method ◽  
Physical Models ◽  
Cmip5 Models ◽  
Steric Sea Level ◽  
Biogeochemical Models ◽  
The Difference

Abstract Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.

scholarly journals Southern Ocean Wind Stress in CMIP5 Models: Role of Wind Fluctuations

2020 ◽  
Vol 33 (4) ◽  
pp. 1209-1226 ◽  
Author(s):  
Xia Lin ◽  
Xiaoming Zhai ◽  
Zhaomin Wang ◽  
David R. Munday
Keyword(s):  
Southern Ocean ◽  
Time Scales ◽  
Wind Stress ◽  
Surface Wind ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Coupled Models ◽  
Surface Wind Stress ◽  
Stress Changes ◽  
The Mean

AbstractThe Southern Ocean (SO) surface wind stress is a major atmospheric forcing for driving the Antarctic Circumpolar Current and the global overturning circulation. Here the effects of wind fluctuations at different time scales on SO wind stress in 18 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are investigated. It is found that including wind fluctuations, especially on time scales associated with synoptic storms, in the stress calculation strongly enhances the mean strength, modulates the seasonal cycle, and significantly amplifies the trends of SO wind stress. In 11 out of the 18 CMIP5 models, the SO wind stress has strengthened significantly over the period of 1960–2005. Among them, the strengthening trend of SO wind stress in one CMIP5 model is due to the increase in the intensity of wind fluctuations, while in all the other 10 models the strengthening trend is due to the increasing strength of the mean westerly wind. These discrepancies in SO wind stress trend in CMIP5 models may explain some of the diverging behaviors in the model-simulated SO circulation. Our results suggest that to reduce the uncertainty in SO responses to wind stress changes in the coupled models, both the mean wind and wind fluctuations need to be better simulated.

scholarly journals The impact of air–sea coupling and ocean biases on the seasonal cycle of southern West African precipitation

2019 ◽  
Vol 53 (11) ◽  
pp. 7027-7044
Author(s):  
Caroline M. Wainwright ◽  
Linda C. Hirons ◽  
Nicholas P. Klingaman ◽  
Richard P. Allan ◽  
Emily Black ◽  
...  
Keyword(s):  
West Africa ◽  
Atlantic Ocean ◽  
Crop Yields ◽  
Coupled Model ◽  
Dry Season ◽  
Global Ocean ◽  
Cmip5 Models ◽  
Rainfall Regime ◽  
Climate Projections ◽  
The Impact

Abstract The biannual seasonal rainfall regime over the southern part of West Africa is characterised by two wet seasons, separated by the ‘Little Dry Season’ in July–August. Lower rainfall totals during this intervening dry season may be detrimental for crop yields over a region with a dense population that depends on agricultural output. Coupled Model Intercomparison Project Phase 5 (CMIP5) models do not correctly capture this seasonal regime, and instead generate a single wet season, peaking at the observed timing of the Little Dry Season. Hence, the realism of future climate projections over this region is questionable. Here, the representation of the Little Dry Season in coupled model simulations is investigated, to elucidate factors leading to this misrepresentation. The Global Ocean Mixed Layer configuration of the Met Office Unified Model is particularly useful for exploring this misrepresentation, as it enables separating the effects of coupled model ocean biases in different ocean basins while maintaining air–sea coupling. Atlantic Ocean SST biases cause the incorrect seasonal regime over southern West Africa. Upper level descent in August reduces ascent along the coastline, which is associated with the observed reduction in rainfall during the Little Dry Season. When coupled model Atlantic Ocean biases are introduced, ascent over the coastline is deeper and rainfall totals are higher during July–August. Hence, this study indicates detrimental impacts introduced by Atlantic Ocean biases, and highlights an area of model development required for production of meaningful climate change projections over the West Africa region.

scholarly journals Effective radiative forcing and adjustments in CMIP6 models

2020 ◽  
Vol 20 (16) ◽  
pp. 9591-9618 ◽  
Author(s):  
Christopher J. Smith ◽  
Ryan J. Kramer ◽  
Gunnar Myhre ◽  
Kari Alterskjær ◽  
William Collins ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Aerosol Forcing ◽  
Intercomparison Project ◽  
Effective Radiative Forcing

Abstract. The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.

A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model

2011 ◽  
Vol 24 (23) ◽  
pp. 6210-6226 ◽  
Author(s):  
S. Zhang
Keyword(s):  
Time Scales ◽  
Parameter Optimization ◽  
Time Scale ◽  
Model Simulation ◽  
Coupled Model ◽  
State Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Long Time ◽  
The Impact

Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.

scholarly journals The Role of Stochastic Forcing in Ensemble Forecasts of the 1997/98 El Niño

2009 ◽  
Vol 22 (10) ◽  
pp. 2526-2540 ◽  
Author(s):  
Li Shi ◽  
Oscar Alves ◽  
Harry H. Hendon ◽  
Guomin Wang ◽  
David Anderson
Keyword(s):  
El Niño ◽  
Initial Conditions ◽  
El Nino ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Ensemble Spread ◽  
Coupled Models ◽  
Central Pacific ◽  
Ensemble Forecasts ◽  
The Impact

Abstract The impact of stochastic intraseasonal variability on the onset of the 1997/98 El Niño was examined using a large ensemble of forecasts starting on 1 December 1996, produced using the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast coupled model. This coupled model has a reasonable simulation of El Niño and the Madden–Julian oscillation, so it provides an ideal framework for investigating the interaction between the MJO and El Niño. The experiment was designed so that the ensemble spread was simply a result of internal stochastic variability that is generated during the forecast. For the initial conditions used here, all forecasts led to warm El Niño–type conditions with the amplitude of the warming varying from 0.5° to 2.7°C in the Niño-3.4 region. All forecasts developed an MJO event during the first 4 months, indicating that perhaps the background state favored MJO development. However, the details of the MJOs that developed during December 1996–March 1997 had a significant impact on the subsequent strength of the El Niño event. In particular, the forecasts with the initial MJOs that extended farther into the central Pacific, on average, led to a stronger El Niño, with the westerly winds in the western Pacific associated with the MJO leading the development of SST and thermocline anomalies in the central and eastern Pacific. These results imply a limit to the accuracy with which the strength of El Niño can be predicted because the details of individual MJO events matter. To represent realistic uncertainty, coupled models should be able to represent the MJO, including its propagation into the central Pacific so that forecasts produce sufficient ensemble spread.

Doubling of future Greenland Ice Sheet surface melt revealed by the new CMIP6 high-emission scenario

2020 ◽  
Author(s):  
Stefan Hofer ◽  
Charlotte Lang ◽  
Charles Amory ◽  
Christoph Kittel ◽  
Alison Delhasse ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
21St Century ◽  
Radiative Forcing ◽  
Climate Models ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Cmip5 Models ◽  
Surface Melt ◽  
The Impact

<p>Future climate projections show a marked increase in Greenland Ice Sheet (GrIS) runoff<br>during the 21st century, a direct consequence of the Polar Amplification signal. Regional<br>climate models (RCMs) are a widely used tool to downscale ensembles of projections from<br>global climate models (GCMs) to assess the impact of global warming on GrIS melt and<br>sea level rise contribution. Initial results of the CMIP6 GCM model intercomparison<br>project have revealed a greater 21st century temperature rise than in CMIP5 models.<br>However, so far very little is known about the subsequent impacts on the future GrIS<br>surface melt and therefore sea level rise contribution. Here, we show that the total GrIS<br>melt during the 21st century almost doubles when using CMIP6 forcing compared to the<br>previous CMIP5 model ensemble, despite an equal global radiative forcing of +8.5 W/m2<br>in 2100 in both RCP8.5 and SSP58.5 scenarios. The total GrIS sea level rise contribution<br>from surface melt in our high-resolution (15 km) projections is 17.8 cm in SSP58.5, 7.9 cm<br>more than in our RCP8.5 simulations, despite the same radiative forcing. We identify a<br>+1.7°C greater Arctic amplification in the CMIP6 ensemble as the main driver behind the<br>presented doubling of future GrIS sea level rise contribution</p>

Impact of sea level data assimilation on the storm surge and seiche forecasts

2020 ◽  
Author(s):  
Marco Bajo ◽  
Iva Međugorac ◽  
Georg Umgiesser ◽  
Mirko Orlić
Keyword(s):  
Sea Level ◽  
Storm Surge ◽  
Ensemble Kalman Filter ◽  
Adriatic Sea ◽  
Initial Error ◽  
Ensemble Spread ◽  
Initial State ◽  
Level Data ◽  
The Impact ◽  
Adriatic Basin

<p>This work assesses the impact of assimilating the sea level data, with an Ensemble Kalman Filter, on storm surge and seiche modelling. The study area is the Adriatic Sea, where seiches are always present after a storm surge, and often overlap on a new storm surge with a possible amplification of the total sea level. Due to errors in the wind and pressure forcing, the forecast of such extreme events is rather challenging in the Adriatic Sea, and a wrong reproduction of such pre-existing seiches reflects on a bad sea-level forecast. Here we show, by two case studies, that the assimilation of sea-level data along the coasts of the Adriatic basin is able to correct the initial state of the hydrodynamic model. Since the initial state is particularly important in the case of pre-existing seiches, the reduction of the initial error propagates several days into the forecast. The two cases here presented were between the most extreme storm surge events in the last years and they both included the pre-existing seiches. The initial forecast was very poor, due to the fact that the wind was underestimated. The assimilation of 3-day long hourly sea level data at eleven stations distributed along the Adriatic coasts produces a better forecast in both cases. Moreover, the ensemble spread allows the uncertainty of the forecast to be estimated, even if the estimate should be calibrated over time in order to be more reliable.</p>

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