Dr Beena Balan Sarojini

Abstract:

We assess for the first time the impact of in-situ ocean observations on European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecasts of both ocean and atmospheric conditions. A series of coupled reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions. Removal of all ocean in-situ observations in the initial conditions leads to significant degradation in the forecasts of ocean surface and subsurface mean state at lead times from week 1 to week 4. The negative impact is predominantly caused by the removal of the Argo observing system in recent decades. Changes in the mean state of atmospheric variables are comparatively small but significant in the forecasts of lower and upper atmospheric circulation over large regions. Our results highlight the value of continuous, real-time in-situ observations of the surface and subsurface ocean for coupled forecasts in the sub-seasonal range.

Abstract:

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss

Abstract:

We propose a new temporal merging method for seamless climate forecast information of climate extremes. In order to construct stable and seamless climate forecast information for user-oriented climate extremes, ensemble members are pooled across available start dates using relative weights, for a target season. Recently, vineyard farmers from Catalonia, Spain, reported frost risk during the spring months (March and April). The observations show high interannual variability of the frequency of frost days (FFD) over Catalonia for the period 1983–2022. Multi-annual to seasonal forecasts from the European Centre for Medium-Range Weather Forecasts show a varying level of FFD forecast skill for lead times 1 to 22 months. The temporally merged weighted forecast enhances forecast skill compared to individual start dates, by adding information from previous start dates, at no additional computational cost. For most start dates, the skill is primarily attributed to the warming trends, except for a few start dates, where remanent forecast skill is attributed to internal variability. The proposed anticipatory framework “Ready-Steady-Action/Go” shows that adopting seamless climate forecast information can bring about 20 to 30% potential economic value for vineyard farmers, with a maximum value noted for multi-annual forecasts.