Contribution of climatic changes in mean and variability to monthly temperature and precipitation extremes

The frequency of climate extremes will change in response to shifts in both mean climate and climate variability. These individual contributions, and thus the fundamental mechanisms behind changes in climate extremes, remain largely unknown. Here we apply the probability ratio concept in large-ensemble climate simulations to attribute changes in extreme events to either changes in mean climate or climate variability. We show that increased occurrence of monthly high-temperature events is governed by a warming mean climate. In contrast, future changes in monthly heavy-precipitation events depend to a considerable degree on trends in climate variability. Spatial variations are substantial however, highlighting the relevance of regional processes. The contributions of mean and variability to the probability ratio are largely independent of event threshold, magnitude of warming and climate model. Hence projections of temperature extremes are more robust than those of precipitation extremes, since the mean climate is better understood than climate variability

MECHANISMS FOR THE EXISTENCE OF DIAGONAL SOUTHERN HEMISPHERE CONVERGENCE ZONES

This thesis considers the northwest-southeast, diagonal, orientation of the South Pacific and South Atlantic Convergence Zones (SPCZ and SACZ, respectively) which provide vital precipitation locally and influence mean climate globally. Their basic formation mechanism is not fully understood. A conceptual framework is developed to explain the mechanism responsible for the SPCZ diagonal orientation. Wind shear and Rossby wave refraction cause vorticity centres in the subtropical jet to develop a diagonal orientation and propagate equatorward towards the eastern Pacific upper-tropospheric westerlies. Ascent ahead of cyclonic vorticity anomalies in the wave then triggers deep convection parallel to the vorticity centre. Latent heat from condensation forces additional ascent and upper-tropospheric divergence; through vortex stretching this leads to an anticyclonic vorticity tendency. The calculation of a vorticity budget shows this tendency is strong enough to dissipate the wave. A similar sequence of events triggers diagonal bands of convection in the SACZ, though the vortex stretching feedback is not strong enough to dissipate the Rossby wave. An atmospheric general circulation model is used to investigate this mechanism. In an experiment the parametrisation of convection is modified: dynamic Rossby wave forcing is decoupled from the usual thermodynamic response. Consequently, Rossby waves over the SPCZ region are not dissipated, confirming the feedback in the framework. Furthermore, it is shown that SPCZ convective events decrease the strength of the eastern Pacific upper-tropospheric westerlies. Further experiments show which surface boundary conditions support the SPCZ diagonal orientation. Continental configuration, orography and absolute Sea Surface Temperatures (SST) do not have a significant influence. The key boundary condition is the zonally asymmetric component of the SST distribution. This leads to a strong subtropical anticyclone over the southeast Pacific that transports and supplies moisture to the SPCZ. Convection is triggered when the dynamical forcing from Rossby waves is present

IGCM4: a fast, parallel and flexible intermediate climate model

The IGCM4 (Intermediate Global Circulation Model version 4) is a global spectral primitive equation climate model whose predecessors have extensively been used in areas such as climate research, process modelling and atmospheric dynamics. The IGCM4’s niche and utility lies in its speed and flexibility allied with the complexity of a primitive equation climate model. Moist processes such as clouds, evaporation, atmospheric radiation and soil moisture are simulated in the model, though in a simplified manner compared to state-of-the-art global circulation models (GCMs). IGCM4 is a parallelised model, enabling both very long integrations to be conducted and the effects of higher resolutions to be explored. It has also undergone changes such as alterations to the cloud and surface processes and the addition of gravity wave drag. These changes have resulted in a significant improvement to the IGCM’s representation of the mean climate as well as its representation of stratospheric processes such as sudden stratospheric warmings. The IGCM4’s physical changes and climatology are described in this paper

The KNMI Large Ensemble Time Slice (KNMI-LENTIS)

Large-ensemble modelling has become an increasingly popular approach to studying the mean climate and the climate system's internal variability in response to external forcing. Here we present the Royal Netherlands Meteorological Institute (KNMI) Large Ensemble Time Slice (KNMI-LENTIS): a new large ensemble produced with the re-tuned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 years each: a present-day time slice and a +2ĝ€¯K warmer future time slice relative to the present day. The initial conditions for the ensemble members are generated with a combination of micro- and macro-perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160ĝ€¯×ĝ€¯10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time slice approach makes it possible to study extreme events on sub-daily timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI-LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The large ensemble is particularly geared towards research in the land-atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI-LENTIS are advised to make in-depth comparisons with observational or reanalysis data, especially if their studies focus on ocean processes, on locations in the Southern Hemisphere, or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI-LENTIS for extreme- and compound-event research and for climate-impact modelling.</p

Subseasonal statistical forecasts of eastern U.S. hot temperature events

Extreme summer temperatures can cause severe societal impacts. Early warnings can aid societal preparedness, but reliable forecasts for extreme temperatures at subseasonal-to-seasonal (S2S) timescales are still missing. Earlier work showed that specific sea surface temperature (SST) patterns over the northern Pacific are precursors of high temperature events in the eastern United States, which might provide skillful forecasts at long-leads (~50 days). However, the verification was based on a single skill metric and a probabilistic forecast was missing. Here, we introduce a novel algorithm that objectively extracts robust precursors from SST linked to a binary target variable. When applied to reanalysis (ERA-5) and climate model data (EC-Earth), we identify robust precursors with the clearest links over the North-Pacific. Different precursors are tested as input for a statistical model to forecast high temperature events. Using multiple skill metrics for verification, we show that daily high temperature events have no predictive skill at long leads. By systematically testing the influence of temporal and spatial aggregation, we find that noise in the target timeseries is an important bottleneck for predicting extreme events on S2S timescales. We show that skill can be increased by a combination of (1) aggregating spatially and/or temporally, (2) lowering the threshold of the target events to increase the base-rate, or (3) add additional variables containing predictive information (soil-moisture). Exploiting these skill-enhancing factors, we obtain forecast skill for moderate heatwaves (i.e. 2 or more hot days closely clustered together in time) up to 50 days lead-time

Characteristics of colliding sea breeze gravity current fronts : a laboratory study

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Quarterly Journal of the Royal Meteorological Society 143 (2017): 1434–1441, doi:10.1002/qj.3015.Sea and land breeze circulations driven by surface temperature differences between land and sea often evolve into gravity currents with sharp fronts. Along narrow peninsulas, islands and enclosed seas, sea/land breeze fronts from opposing shorelines may converge and collide and may initiate deep convection and heavy precipitation. Here we investigate the collision of two sea breeze gravity current fronts in an analogue laboratory setting. We examine these collisions by means of ‘lock-exchange’ experiments in a rectangular channel. The effects of differences in gravity current density and height are studied. Upon collision, a sharp front separating the two currents develops. For symmetric collisions (the same current densities and heights) this front is vertical and stationary. For asymmetric collisions (density differences, similar heights) the front is tilted, changes shape in time and propagates in the same direction as the heavier current before the collision. Both symmetric and asymmetric collisions lead to upward displacement of fluid from the gravity currents and mixing along the plane of contact. The amount of mixing along the collision front decreases with asymmetry. Height differences impact post-collision horizontal propagation: there is significant propagation in the same direction as the higher current before collision, independent of density differences. Collisions of two gravity current fronts force sustained ascending motions which increase the potential for deep convection. From our experiments we conclude that this potential is larger in stationary collision fronts from symmetric sea breeze collisions than in propagating collision fronts from asymmetric sea breeze collisions.National Science Foundation Grant Number: OCE-0824636; Office of Naval Research Grant Number: N00014-09-1-0844; National Aeronautics and Space Administration Grant Number: NASA NNX14A078

A data-driven model for Fennoscandian wildfire danger

Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001-2019) satellite-based fire occurrence dataset at a 0.25° spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios

The Climatological Renewable Energy Deviation Index

Here we propose an index to quantify and analyse the impact of climatological variability on the energy system at different timescales. We define the Climatological Renewable Energy Deviation Index (CREDI) as the cumulative anomaly of a renewable resource with respect to its climate over a specific time period of interest. We analyse the index at decadal, annual and (sub-)seasonal timescales using the forthcoming Pan-European Climate Database and consider the starting point and window of analysis for its use at those timescales. The CREDI is meant as an analytical tool for researchers and stakeholders to help them quantify, understand, and explain, the impact of the variability of weather on the energy system across timescales. Improved understanding translates to better assessments of how renewable resources, and the associated risks for energy security, may fare in current and future climatological settings. The practical use of the index is in resource planning. For example transmission system operators may be able to adjust short-term planning to reduce adequacy issues before they occur or combine the index with storyline event selection for improved assessments of climate change related risks

The influence of weather regimes on European renewable energy production and demand

The growing share of variable renewable energy increases the meteorological sensitivity of power systems. This study investigates if large-scale weather regimes capture the influence of meteorological variability on the European energy sector. For each weather regime, the associated changes to wintertime -mean and extreme- wind and solar power production, temperature-driven energy demand and energy shortfall (residual load) are explored. Days with a blocked circulation pattern, i.e. the Scandinavian Blocking and NAO negative regimes, on average have lower than normal renewable power production, higher than normal energy demand and therefore, higher than normal energy shortfall. These average effects hide large variability of energy parameters within each weather regime. Though the risk of extreme high energy shortfall events increases in the two blocked regimes (by a factor of 2.0 and 1.5, respectively), it is shown that such events occur in all regimes. Extreme high energy shortfall events are the result of rare circulation types and smaller-scale features, rather than extreme magnitudes of common large-scale circulation types. In fact, these events resemble each other more strongly than their respective weather regime mean pattern. For (sub-)seasonal forecasting applications weather regimes may be of use for the energy sector. At shorter lead times or for more detailed system analyses, their ineffectiveness at characterising extreme events limits their potential