Decadal prediction of the North Atlantic subpolar gyre in the HiGEM high-resolution climate model

This paper presents an analysis of initialised decadal hindcasts of the North Atlantic subpolar gyre (SPG) using the HiGEM model, which has a nominal grid-spacing of 90 km in the atmosphere, and 1/3 ∘∘ in the ocean. HiGEM decadal predictions (HiGEM-DP) exhibit significant skill at capturing 0–500 m ocean heat content in the SPG, and outperform historically forced transient integrations and persistence for up to a decade ahead. An analysis of case-studies of North Atlantic decadal change, including the 1960s cooling, the mid-1990s warming, and the post-2005 cooling, show that changes in ocean circulation and heat transport dominate the predictions of the SPG. However, different processes are found to dominate heat content changes in different regions of the SPG. Specifically, ocean advection dominates in the east, but surface fluxes dominate in the west. Furthermore, compared to previous studies, we find a smaller role for ocean heat transport changes due to ocean circulation anomalies at the latitudes of the SPG, and, for the 1960s cooling, a greater role for surface fluxes. Finally, HiGEM-DP predicts the observed positive state of the North Atlantic Oscillation in the early 1990s. These results support an important role for the ocean in driving past changes in the North Atlantic region, and suggest that these changes were predictable

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)

Recent progress in understanding and predicting Atlantic decadal climate variability

Recent Atlantic climate prediction studies are an exciting new contribution to an extensive body of research on Atlantic decadal variability and predictability that has long emphasized the unique role of the Atlantic Ocean in modulating the surface climate. We present a survey of the foundations and frontiers in our understanding of Atlantic variability mechanisms, the role of the Atlantic Meridional Overturning Circulation (AMOC), and our present capacity for putting that understanding into practice in actual climate prediction systems

Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this recordThe decline of Arctic sea ice is an integral part of anthropogenic climate change. Sea-ice loss is already having a significant impact on Arctic communities and ecosystems. Its role as a cause of climate changes outside of the Arctic has also attracted much scientific interest. Evidence is mounting that Arctic sea-ice loss can affect weather and climate throughout the Northern Hemisphere. The remote impacts of Arctic sea-ice loss can only be properly represented using models that simulate interactions among the ocean, sea ice, land and atmosphere. A synthesis of six such experiments with different models shows consistent hemispheric-wide atmospheric warming, strongest in the mid-to-high-latitude lower troposphere; an intensification of the wintertime Aleutian Low and, in most cases, the Siberian High; a weakening of the Icelandic Low; and a reduction in strength and southward shift of the mid-latitude westerly winds in winter. The atmospheric circulation response seems to be sensitive to the magnitude and geographic pattern of sea-ice loss and, in some cases, to the background climate state. However, it is unclear whether current-generation climate models respond too weakly to sea-ice change. We advocate for coordinated experiments that use different models and observational constraints to quantify the climate response to Arctic sea-ice loss.J.A.S. and R.B. were funded by the Natural Environment Research Council (NE/P006760/1). C.D. acknowledges the National Science Foundation (NSF), which sponsors the National Center for Atmospheric Research. D.M.S. was supported by the Met Office Hadley Centre Climate Programme (GA01101) and the APPLICATE project, which is funded by the European Union’s Horizon 2020 programme. X.Z. was supported by the NSF (ARC#1023592). P.J.K. and K.E.M. were supported by the Canadian Sea Ice and Snow Evolution Network, which is funded by the Natural Science and Engineering Research Council of Canada. T.O. was funded by Environment and Climate Change Canada (GCXE17S038). L.S. was supported by the National Oceanic and Atmospheric Administration’s Climate Program Office

Seasonal and decadal forecasts of Atlantic Sea surface temperatures using a linear inverse model

Predictability of Atlantic Ocean sea surface temperatures (SST) on seasonal and decadal timescales is investigated using a suite of statistical linear inverse models (LIM). Observed monthly SST anomalies in the Atlantic sector (between 22(Formula presented.)S and 66(Formula presented.)N) are used to construct the LIMs for seasonal and decadal prediction. The forecast skills of the LIMs are then compared to that from two current operational forecast systems. Results indicate that the LIM has good forecast skill for time periods of 3–4 months on the seasonal timescale with enhanced predictability in the spring season. On decadal timescales, the impact of inter-annual and intra-annual variability on the predictability is also investigated. The results show that the suite of LIMs have forecast skill for about 3–4 years over most of the domain when we use only the decadal variability for the construction of the LIM. Including higher frequency variability helps improve the forecast skill and maintains the correlation of LIM predictions with the observed SST anomalies for longer periods. These results indicate the importance of temporal scale interactions in improving predictability on decadal timescales. Hence, LIMs can not only be used as benchmarks for estimates of statistical skill but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components

Implementing a Single-Session Nurse-Led Assessment Clinic into a Gender Service.

The Royal Children's Hospital Gender Service offers support, assessment, and medical care to transgender and gender diverse children and adolescents in Victoria, Australia. Referrals have rapidly increased leading to extended wait times. In response, a single-session nurse-led assessment clinic (SSNac) was introduced as the clinical entry point to the service, during which a biopsychosocial assessment is undertaken, and information, education, and support are provided. Outcomes of the SSNac include a significant reduction in wait times and a timely clinical triage system. This article documents the creation and implementation of SSNac to offer a template for use in other gender services