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Xiaohe An, Bo Wu, Tianjun Zhou, and Bo Liu

Abstract

The interdecadal Pacific oscillation (IPO) and Atlantic multidecadal oscillation (AMO), two leading modes of decadal climate variability, are not independent. It was proposed that ENSO-like sea surface temperature (SST) variations play a central role in the Pacific responses to the AMO forcing. However, observational analyses indicate that the AMO-related SST anomalies in the tropical Pacific are far weaker than those in the extratropical North Pacific. Here, we show that SST in the North Pacific is tied to the AMO forcing by convective heating associated with precipitation over the tropical Pacific, instead of by SST there, based on an ensemble of pacemaker experiments with North Atlantic SST restored to the observation in a coupled general circulation model. The AMO modulates precipitation over the equatorial and tropical southwestern Pacific through exciting an anomalous zonal circulation and an interhemispheric asymmetry of net moist static energy input into the atmosphere. The convective heating associated with the precipitation anomalies drives SST variations in the North Pacific through a teleconnection, but it remarkably weakens the ENSO-like SST anomalies through a thermocline damping effect. This study has implications that the IPO is a combined mode generated by both AMO forcing and local air–sea interactions, but the IPO-related global warming acceleration/slowdown is independent of the AMO.

Open access
Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Mark A. Collier, Russell Fiedler, Thomas S. Moore, Christopher C. Chapman, Bernadette M. Sloyan, and Richard J. Matear

Abstract

We detail the system design, model configuration, and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1). CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multiyear climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to the present. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are subsampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross-domain covariances between ocean, atmosphere, sea ice, and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean, and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions, potentially enabling individual forecasts for all members each month over the 60-yr period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

Open access
Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Dougal T. Squire, Mark A. Collier, Christopher C. Chapman, Russell Fiedler, Dylan Harries, Thomas S. Moore, Doug Richardson, James S. Risbey, Benjamin J. E. Schroeter, Serena Schroeter, Bernadette M. Sloyan, Carly Tozer, Ian G. Watterson, Amanda Black, Courtney Quinn, and Richard J. Matear

Abstract

The CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatiotemporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere, and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large-scale ocean structure, transports, and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration, and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive, and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multiyear to decadal time scales.

Open access
Cheng Shen, Jinlin Zha, Jian Wu, and Deming Zhao

Abstract

Investigations of variations and causes of near-surface wind speed (NWS) further understanding of atmospheric changes and improve the ability of climate analysis and projections. NWS varies on multiple temporal scales; however, the centennial-scale variability in NWS and associated causes over China remains unknown. In this study, we employ the European Centre for Medium-Range Weather Forecasts (ECMWF) Twentieth Century Reanalysis (ERA-20C) to study the centennial-scale changes in NWS from 1900 to 2010. Meanwhile, a forward stepwise regression algorithm is used to reveal the relationships between NWS and large-scale ocean–atmosphere circulations. The results show three unique periods in annual mean NWS over China from 1900 to 2010. The annual mean NWS displayed decreasing trends of −0.87% and −11.75% decade−1 from 1900 to 1925 and from 1957 to 2010, respectively, which were caused by the decreases in the days with strong winds, with trends of −6.64 and −4.66 days decade−1, respectively. The annual mean NWS showed an upward trend of 55.47% decade−1 from 1926 to 1956, which was caused by increases in the days with moderate (0.43 days decade−1) and strong winds (23.55 days decade−1). The reconstructed wind speeds based on forward stepwise regression algorithm matched well with the original wind speeds; therefore, the decadal changes in NWS over China at the centennial scale were mainly induced by large-scale ocean–atmosphere circulations, with the total explanation power of 66%. The strongest explanation power was found in winter (74%), and the weakest explanation power was found in summer (46%).

Open access
David Leutwyler, Adel Imamovic, and Christoph Schär

Abstract

Soil moisture–atmosphere interactions are key elements of the regional climate system. There is a well-founded hope that a more accurate representation of the soil moisture–precipitation feedback would improve the simulation of summer precipitation on daily to seasonal, to climate time scales. However, uncertainties have persistently remained as the simulated feedback is strongly sensitive to the model representation of deep convection. Here we assess the feedback representation using a GPU-accelerated version of the regional climate model COSMO. We simulate and compare the impact of continental-scale springtime soil moisture anomalies on summer precipitation at convection-resolving (2.2 km) and convection-parameterizing resolution (12 km). We conduct reanalysis-driven simulations of 10 summer seasons (1999–2008) in continental Europe. While both simulations qualitatively agree on a positive sign of soil moisture–induced precipitation, they strongly differ in precipitation frequency: when convection is parameterized, wetter soil predominantly leads to more frequent precipitation events, and when convection is treated explicitly, they primarily become more intense. The results indicate that the sensitivity to soil moisture is stronger with parameterized convection, suggesting that the land surface–atmosphere coupling may be overestimated. In addition, the feedback’s sensitivity in complex terrain is assessed for soil perturbations of different horizontal scales. The convection-resolving simulations confirm a negative feedback for subcontinental soil moisture anomalies, which manifests itself in a local decrease of wet-hour frequency. However, the intensity feedback reinforces precipitation events at the same time (positive feedback). The two processes are represented differently in simulations with explicit and parameterized convection, explaining much of the difference between the two simulations.

Open access
Jake Aylmer, David Ferreira, and Daniel Feltham
Open access
Eviatar Bach, Safa Mote, V. Krishnamurthy, A. Surjalal Sharma, Michael Ghil, and Eugenia Kalnay

Abstract

Oscillatory modes of the climate system are among its most predictable features, especially at intraseasonal time scales. These oscillations can be predicted well with data-driven methods, often with better skill than dynamical models. However, since the oscillations only represent a portion of the total variance, a method for beneficially combining oscillation forecasts with dynamical forecasts of the full system was not previously known. We introduce Ensemble Oscillation Correction (EnOC), a general method to correct oscillatory modes in ensemble forecasts from dynamical models. We compute the ensemble mean—or the ensemble probability distribution—with only the best ensemble members, as determined by their discrepancy from a data-driven forecast of the oscillatory modes. We also present an alternate method that uses ensemble data assimilation to combine the oscillation forecasts with an ensemble of dynamical forecasts of the system (EnOC-DA). The oscillatory modes are extracted with a time series analysis method called multichannel singular spectrum analysis (M-SSA), and forecast using an analog method. We test these two methods using chaotic toy models with significant oscillatory components and show that they robustly reduce error compared to the uncorrected ensemble. We discuss the applications of this method to improve prediction of monsoons as well as other parts of the climate system. We also discuss possible extensions of the method to other data-driven forecasts, including machine learning.

Open access
Robert Ricker, Frank Kauker, Axel Schweiger, Stefan Hendricks, Jinlun Zhang, and Stephan Paul

Abstract

We investigate how sea ice decline in summer and warmer ocean and surface temperatures in winter affect sea ice growth in the Arctic. Sea ice volume changes are estimated from satellite observations during winter from 2002 to 2019 and are partitioned into thermodynamic growth and dynamic volume change. Both components are compared with validated sea ice–ocean models forced by reanalysis data to extend observations back to 1980 and to understand the mechanisms that cause the observed trends and variability. We find that a negative feedback driven by the increasing sea ice retreat in summer yields increasing thermodynamic ice growth during winter in the Arctic marginal seas eastward from the Laptev Sea to the Beaufort Sea. However, in the Barents and Kara Seas, this feedback seems to be overpowered by the impact of increasing oceanic heat flux and air temperatures, resulting in negative trends in thermodynamic ice growth of −2 km3 month−1 yr−1 on average over 2002–19 as derived from satellite observations.

Open access
Svenya Chripko, Rym Msadek, Emilia Sanchez-Gomez, Laurent Terray, Laurent Bessières, and Marie-Pierre Moine

Abstract

The Northern Hemisphere transient atmospheric response to Arctic sea decline is investigated in autumn and winter, using sensitivity experiments performed with the CNRM-CM6-1 high-top climate model. Arctic sea ice albedo is reduced to the ocean value, yielding ice-free conditions during summer and a more moderate sea ice reduction during the following months. A strong amplification of temperatures over the Arctic is induced by sea ice loss, with values reaching up to 25°C near the surface in autumn. Significant surface temperature anomalies are also found over the midlatitudes, with a warming reaching 1°C over North America and Europe, and a cooling reaching 1°C over central Asia. Using a dynamical adjustment method based on a regional reconstruction of circulation analogs, we show that the warming over North America and Europe can be explained both by changes in the atmospheric circulation and by the advection of warmer oceanic air by the climatological flow. In contrast, we demonstrate that the sea ice–induced cooling over central Asia is solely due to dynamical changes, involving an intensification of the Siberian high and a cyclonic anomaly over the Sea of Okhotsk. In the troposphere, the abrupt Arctic sea ice decline favors a narrowing of the subtropical jet stream and a slight weakening of the lower part of the polar vortex that is explained by a weak enhancement of upward wave activity toward the stratosphere. We further show that reduced Arctic sea ice in our experiments is mainly associated with less severe cold extremes in the midlatitudes.

Open access
Wenhao Dong, Ming Zhao, Yi Ming, and V. Ramaswamy

Abstract

The characteristics of tropical mesoscale convective systems (MCSs) simulated with a finer-resolution (~50 km) version of the Geophysical Fluid Dynamics Laboratory (GFDL) AM4 model are evaluated by comparing with a comprehensive long-term observational dataset. It is shown that the model can capture the various aspects of MCSs reasonably well. The simulated spatial distribution of MCSs is broadly in agreement with the observations. This is also true for seasonality and interannual variability over different land and oceanic regions. The simulated MCSs are generally longer-lived, weaker, and larger than observed. Despite these biases, an event-scale analysis suggests that their duration, intensity, and size are strongly correlated. Specifically, longer-lived and stronger events tend to be bigger, which is consistent with the observations. The same model is used to investigate the response of tropical MCSs to global warming using time-slice simulations forced by prescribed sea surface temperatures and sea ice. There is an overall decrease in occurrence frequency, and the reduction over land is more prominent than over ocean.

Open access