The MOSAiC ice floe: sediment-laden survivor from the Siberian shelf

In September 2019, the research icebreaker Polarstern started the largest multidisciplinary Arctic expedition so far, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to an ice floe for a whole year, thus including the winter season, the declared goal of the expedition is to better understand and quantify relevant processes within the atmosphere-ice-ocean system that impact the sea ice mass and energy budget, ultimately leading to much improved climate models. Satellite observations, atmospheric reanalysis data, and readings from a nearby meteorological station indicate that the interplay of high ice export in late winter and exceptionally high air temperatures resulted in the longest ice-free summer period since reliable instrumental records began. We show, using a Lagrangian tracking tool and a thermodynamic sea ice model, that the MOSAiC floe carrying the Central Observatory (CO) formed in a polynya event north of the New Siberian Islands at the beginning of December 2018. The results further indicate that sea ice in the vicinity of the CO (

Sources of Bias in the Monthly CFSv2 Forecast Climatology

Forecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.

The Role of Large-Scale Feedbacks in Cumulus Convection Parameter Estimation

Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that use empirically set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model that includes large-scale feedbacks for cumulus convection and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.

Impact of Climate Drift on Twenty-First-Century Projection in a Coupled Atmospheric–Ocean General Circulation Model

Reducing climate drift in coupled atmosphere–ocean general circulation models (AOGCMs) usually requires 1000–2000 years of spinup, which has not been practical for every modeling group to do. For the purpose of evaluating the impact of climate drift, the authors have performed a multimillennium-long control run of the Goddard Institute for Space Studies model (GISS-EH) AOGCM and produced different twentieth-century historical simulations and subsequent twenty-first-century projections by branching off the control run at various stages of equilibration. The control run for this model is considered at quasi equilibration after a 1200-yr spinup from a cold start. The simulations that branched off different points after 1200 years are robust, in the sense that their ensemble means all produce the same future projection of warming, both in the global mean and in spatial detail. These robust projections differ from the one that was originally submitted to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), which branched off a not-yet-equilibrated control run. The authors test various common postprocessing schemes in removing climate drift caused by a not-yet-equilibrated ocean initial state and find them to be ineffective, judging by the fact that they differ from each other and from the robust results that branched off an equilibrated control. The authors' results suggest that robust twenty-first-century projections of the forced response can be achieved by running climate simulations from an equilibrated ocean state, because memory of the different initial ocean state is lost in about 40 years if the forced run is started from a quasi-equilibrated state.

Climate Drift in the CMIP3 Models

Even in the absence of external forcing, climate models often exhibit long-term trends that cannot be attributed to natural variability. This so-called climate drift arises for various reasons including the following: perturbations to the climate system on coupling component models together and deficiencies in model physics and numerics. When examining trends in historical or future climate simulations, it is important to know the error introduced by drift so that action can be taken where necessary. This study assesses the importance of drift for a number of climate properties at global and local scales. To illustrate this, the present paper focuses on simulated trends over the second half of the twentieth century. While drift in globally averaged surface properties is generally considerably smaller than observed and simulated twentieth-century trends, it can still introduce nontrivial errors in some models. Furthermore, errors become increasingly important at smaller scales. The direction of drift is not systematic across different models or variables, as such drift is considerably reduced in the multimodel mean. Despite drift being primarily associated with ocean adjustment, it is also apparent in atmospheric variables. For example, most models have local drift magnitudes in surface air and ocean temperatures that are typically between 15% and 35% of the twentieth-century simulation trend magnitudes for 1950–2000. Below depths of 1000–2000 m, drift dominates over any forced trend in most regions. As such steric sea level is strongly affected and for some models and regions the sea level trend direction is reversed. Thus depending on the application, drift may be negligible or may make up an important part of the simulated trend.

Land Surface Initialization Using an Offline CLM3 Simulation with the GSWP-2 Forcing Dataset and Its Impact on CAM3 Simulations of the Boreal Summer Climate*

The impacts of initialized land surface conditions on the monthly prediction were investigated using ensemble simulations from the Community Atmosphere Model version 3 (CAM3). The land surface initialization was based on an offline calculation of Community Land Model version 3 driven by observation-based meteorological forcings from the Global Soil Wetness Project 2 (GSWP2). A simple but effective correction method was applied to the GSWP2 forcings prior to the offline calculation to reduce the discrepancies between the observation-forced land surface conditions and the modeling system, which can cause climate drift and initial shock problems. The climatological mean of GSWP2 forcings was adjusted to that of the target model (CAM3), while the monthly anomalies were scaled to the model statistics and high-frequency synoptic variabilities were included. Ensemble hindcast experiments with and without land surface initialization were conducted for the boreal summer (May–September), for 1983–95. The initialization process is shown to prevent climate drift and to transfer the atmospheric anomalies to the land surface memory. Statistical analyses of the simulation results reveal that the land surface initialization increased the externally forced variance over most continental regions, which is translated to enhanced potential predictability, particularly for regions with strong land–atmosphere coupling.

Low-Order Stochastic Mode Reduction for a Prototype Atmospheric GCM

This study applies a systematic strategy for stochastic modeling of atmospheric low-frequency variability to a three-layer quasigeostrophic model. This model climate has reasonable approximations of the North Atlantic Oscillation (NAO) and Pacific–North America (PNA) patterns. The systematic strategy consists first of the identification of slowly evolving climate modes and faster evolving nonclimate modes by use of an empirical orthogonal function (EOF) decomposition in the total energy metric. The low-order stochastic climate model predicts the evolution of these climate modes a priori without any regression fitting of the resolved modes. The systematic stochastic mode reduction strategy determines all correction terms and noises with minimal regression fitting of the variances and correlation times of the unresolved modes. These correction terms and noises account for the neglected interactions between the resolved climate modes and the unresolved nonclimate modes. Low-order stochastic models with 10 or less resolved modes capture the statistics of the original model very well, including the variances and temporal correlations with high pattern correlations of the transient eddy fluxes. A budget analysis establishes that the low-order stochastic models are highly nonlinear with significant contributions from both additive and multiplicative noise. This is in contrast to previous stochastic modeling studies. These studies a priori assume a linear model with additive noise and regression fit the resolved modes. The multiplicative noise comes from the advection of the resolved modes by the unresolved modes. The most straightforward low-order stochastic climate models experience climate drift that stems from the bare truncation dynamics. Even though the geographic correlation of the transient eddy fluxes is high, they are underestimated by a factor of about 2 in the a priori procedure and thus cannot completely overcome the large climate drift in the bare truncation. Also, variants of the reduced stochastic modeling procedure that experience no climate drift with good predictions of both the variances and time correlations are discussed. These reduced models without climate drift are developed by slowing down the dynamics of the bare truncation compared with the interactions with the unresolved modes and yield a minimal two-parameter regression fitting strategy for the climate modes. This study points to the need for better optimal basis functions that optimally capture the essential slow dynamics of the system to obtain further improvements for the reduced stochastic modeling procedure.