Evolution and modulation of tropical heating from the last glacial maximum through the twenty-first century

Twentieth century observations show that during the last 50?years the sea-surface temperature (SST) of the tropical oceans has increased by ~0.5°C and the area of SST >26.5 and 28°C (arbitrarily referred to as the oceanic warm pool: OWP) by 15 and 50% respectively in association with an increase in green house gas concentrations, with non-understood natural variability or a combination of both. Based on CMIP3 projections the OWP is projected to double during twenty-first century in a moderate CO₂ forcing scenario (IPCC A1B scenario). However, during the observational period the area of positive atmospheric heating (referred to as the dynamic warm pool, DWP), has remained constant. The threshold SST (T _H ), which demarks the region of net heating and cooling, has increased from 26.6°C in the 1950s to 27.1°C in the last decade and it is projected to increase to ~28.5°C by 2100. Based on climate model simulations, the area of the DWP is projected to remain constant during the twenty-first century. Analysis of the paleoclimate model intercomparison project (PMIP I and II) simulations for the Last Glacial maximum and the Mid-Holocene periods show a very similar behaviour, with a larger OWP in periods of elevated tropical SST, and an almost constant DWP associated with a varying T _H . The constancy of the DWP area, despite shifts in the background SST, is shown to be the result of a near exact matching between increases in the integrated convective heating within the DWP and the integrated radiative cooling outside the DWP as SST changes. Although the area of the DWP remains constant, the total tropical atmospheric heating is a strong function of the SST. For example the net heating has increased by about 10% from 1950 to 2000 and it is projected to increase by a further 20% by 2100. Such changes must be compensated by a more vigorous atmospheric circulation, with growth in convective heating within the warm pool, and an increase of subsiding air and stability outside the convective warm pool and an increase of vertical shear at the DWP boundaries. This finding is contrary to some conclusions from other studies but in accord with others. We discuss the similarities and differences at length.     

Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982–2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere–ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.     

Statistical Downscaling of Wind Variability from Meteorological Fields

Measurements show that on numerous occasions the low-level wind is highly variable across a large portion of south-eastern Australia. Under such conditions the risk of a large rapid change in total wind power is increased. While variability tends to increase with mean wind speed, a large component of wind variability is not explained by wind speed alone. In this work, reanalysis fields from the US National Centers for Environmental Prediction (NCEP) are statistically downscaled to model wind variability at a coastal location in Victoria, Australia. In order to reduce the dimensionality of the problem, the NCEP fields are each decomposed using empirical orthogonal function (EOF) techniques. The downscaling technique is applied to two periods in the seasonal cycle, namely (i) winter to early spring, and (ii) summer. In each case, data representing 2 years are used to form a model that is then validated using independent data from another year. The EOFs that best predict wind variability are examined. To allow for non-linearity and complex interaction between variables, all empirical models are built using random forests. Quantitatively, the model compares favourably with a simple regression of wind variability against wind speed, as well as multiple linear regression models.     

Ocean–atmosphere coupling and the boreal winter MJO

The influence of ocean–atmosphere coupling on the simulation and prediction of the boreal winter Madden–Julian Oscillation (MJO) is examined using the Seoul National University coupled general circulation model (CGCM) and atmospheric—only model (AGCM). The AGCM is forced with daily SSTs interpolated from pentad mean CGCM SSTs. Forecast skill is examined using serial extended simulations spanning 26 different winter seasons with 30-day forecasts commencing every 5 days providing a total of 598 30-day simulations. By comparing both sets of experiments, which share the same atmospheric components, the influence of coupled ocean–atmosphere processes on the simulation and prediction of MJO can be studied. The mean MJO intensity possesses more realistic amplitude in the CGCM than in AGCM. In general, the ocean–atmosphere coupling acts to improve the simulation of the spatio-temporal evolution of the eastward propagating MJO and the phase relationship between convection (OLR) and SST over the equatorial Indian Ocean and the western Pacific. Both the CGCM and observations exhibit a near-quadrature relationship between OLR and SST, with the former lagging by about two pentads. However, the AGCM shows a less realistic phase relationship. As the initial conditions are the same in both models, the additional forcing by SST anomalies in the CGCM extends the prediction skill beyond that of the AGCM. To test the applicability of the CGCM to real-time prediction, we compute the Real-time Multivariate MJO (RMM) index and compared it with the index computed from observations. RMM1 (RMM2) falls away rapidly to 0.5 after 17–18 (15–16) days in the AGCM and 18–19 (16–17) days in the CGCM. The prediction skill is phase dependent in both the CGCM and AGCM.     

Participatory modeling in dairy farm systems: a method for building consensual environmental sustainability using seasonal climate forecasts

Dairy farmers face increasing pressure to decrease environmental impact while remaining economically viable. Adaptation of farm management practices in response to seasonal climate forecasts may be one means of achieving these objectives. This paper describes the interactive and iterative process by which farmers, researchers, extension agents, regulatory agencies, and other stakeholders collaborated to create, calibrate, and validate the Dynamic North Florida Dairy Farm model (DyNoFlo), a whole-farm decision support system to decrease nitrogen leaching while maintaining profitability under variable climate conditions. Participatory modeling may enhance the creation of adoptable and adaptable user-friendly models that include environmental, economic and biophysical components. By providing farmers, policy makers, and other stakeholders with a more holistic view of current practices, common ground among them was more easily identified and collaboration was fostered. Farmer values included willingness to be good environmental stewards when they are profitable. The participatory research and development process enhanced understanding of and potential adaptation to seasonal climate variability conditioned to the El Niño Southern Oscillation (ENSO) phases in light of increasing environmental regulations and economic challenges. Adoption of the collaboratively-developed DyNoFlo is expected to be higher than usual because stakeholders feel greater ownership of the final product.     

A robust sequential CO2 emissions strategy based on optimal control of atmospheric CO2 concentrations

This paper formally introduces the concept of mitigation as a stochastic control problem. This is illustrated by applying a digital state variable feedback control approach known as Non-Minimum State Space (NMSS) control to the problem of specifying carbon emissions to control atmospheric CO₂ concentrations in the presence of uncertainty. It is shown that the control approach naturally lends itself to integrating both anticipatory and reflexive mitigation strategies within a single unified framework. The framework explicitly considers the closed-loop nature of climate mitigation, and employs a policy orientated optimisation procedure to specify the properties of this closed-loop system. The product of this exercise is a control law that is suitably conditioned to regulate atmospheric CO₂ concentrations through assimilating online information within a 25-year review cycle framework. It is shown that the optimal control law is also robust when faced with significant levels of uncertainty about the functioning of the global carbon cycle.     

Conditional probabilistic estimates of 21st century greenhouse gas emissions based on the storylines of the IPCC-SRES scenarios

The conditional probabilistic scenario analysis combines statistical methods of uncertainty analysis at parameter level with storylines which recognize the deep uncertainty that exists for several underlying trends. The model calculations indicate that cumulative 21st century emissions could range from 800 to 2500 GtC in the absence of climate policy. This range originates partly from the underlying storylines, and partly from the probabilistic analysis. Among the most important parameters contributing to the uncertainty range are uncertainty in income growth, population growth, parameters determining energy demand, oil resources and fuel preferences. The contribution of these factors is also scenario-dependent.     

Preface: Workshop on matter,astrophysics, gravitation,ions and cosmology—MAGIC23

We outline our experience in organizing the first edition of the Workshop on Matter, Astrophysics, Gravitation, Ions and Cosmology, held in virtual and in-person format, denominated MAGIC23, held from 6 to 10 March, 2023, in Praia do Rosa, Santa Catarina, Brazil. The event aimed to bring together leading academic scientists, professors, students, and research scholars for exchanging experiences and discuss the most recent innovations, trends, practical challenges, and experimental and theoretical solutions adopted in the investigation fields within the scope of the meeting. The workshop offered to the participants a platform for scientific and academic projects, partnerships, and presentation of high-quality research contributions describing original and unpublished results on topics related to matter, astrophysics, gravitation, ions, and cosmology.     

An Integrated Assessment of changes in the thermohaline circulation

This paper discusses the risks of a shutdown of the thermohaline circulation (THC) for the climate system, for ecosystems in and around the North Atlantic as well as for fisheries and agriculture by way of an Integrated Assessment. The climate model simulations are based on greenhouse gas scenarios for the 21st century and beyond. A shutdown of the THC, complete by 2150, is triggered if increased freshwater input from inland ice melt or enhanced runoff is assumed. The shutdown retards the greenhouse gas-induced atmospheric warming trend in the Northern Hemisphere, but does not lead to a persistent net cooling. Due to the simulated THC shutdown the sea level at the North Atlantic shores rises by up to 80 cm by 2150, in addition to the global sea level rise. This could potentially be a serious impact that requires expensive coastal protection measures. A reduction of marine net primary productivity is associated with the impacts of warming rather than a THC shutdown. Regional shifts in the currents in the Nordic Seas could strongly deteriorate survival chances for cod larvae and juveniles. This could lead to cod fisheries becoming unprofitable by the end of the 21st century. While regional socioeconomic impacts might be large, damages would be probably small in relation to the respective gross national products. Terrestrial ecosystem productivity is affected much more by the fertilization from the increasing CO₂ concentration than by a THC shutdown. In addition, the level of warming in the 22nd to 24th century favours crop production in northern Europe a lot, no matter whether the THC shuts down or not. CO₂ emissions corridors aimed at limiting the risk of a THC breakdown to 10% or less are narrow, requiring departure from business-as-usual in the next few decades. The uncertainty about THC risks is still high. This is seen in model analyses as well as in the experts’ views that were elicited. The overview of results presented here is the outcome of the Integrated Assessment project INTEGRATION.