scholarly journals Impact of Global Ocean Surface Warming on Seasonal-to-Interannual Climate Prediction

2011 ◽  
Vol 24 (6) ◽  
pp. 1626-1646 ◽  
Author(s):  
Jing-Jia Luo ◽  
Swadhin K. Behera ◽  
Yukio Masumoto ◽  
Toshio Yamagata
Keyword(s):  
Global Warming ◽  
Initial Conditions ◽  
Warming Trend ◽  
Climate Prediction ◽  
Global Ocean ◽  
Lead Times ◽  
Subsurface Temperature ◽  
Substantial Impact ◽  
Climate Predictability ◽  
Sst Warming

Abstract Surface air temperature (SAT) over the globe, particularly the Northern Hemisphere continents, has rapidly risen over the last 2–3 decades, leading to an abrupt shift toward a warmer climate state after 1997/98. Whether the terrestrial warming might be caused by local response to increasing greenhouse gas (GHG) concentrations or by sea surface temperature (SST) rise is recently in dispute. The SST warming itself may be driven by both the increasing GHG forcing and slowly varying natural processes. Besides, whether the recent global warming might affect seasonal-to-interannual climate predictability is an important issue to be explored. Based on the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) climate prediction system in which only observed SSTs are assimilated for coupled model initialization, the present study shows that the historical SST rise plays a key role in driving the intensified terrestrial warming over the globe. The SST warming trend, while negligible for short lead predictions, has substantial impact on the climate predictability at long lead times (>1 yr) particularly in the extratropics. The tropical climate predictability, however, is little influenced by global warming. Given a perfect warming trend and/or a perfect model, global SAT and precipitation could be predicted beyond two years in advance with an anomaly correlation skill above ∼0.6. Without assimilating ocean subsurface observations, model initial conditions show a strong spurious cooling drift of subsurface temperature; this is caused by large negative surface heat flux damping arising from the SST-nudging initialization. The spurious subsurface cooling drift acts to weaken the initial SST warming trend during model forecasts, leading to even negative trends of global SAT and precipitation at long lead times and hence deteriorating the global climate predictability. Concerning the important influence of the subsurface temperature on the global SAT trend, future efforts are required to develop a good scheme for assimilating subsurface information particularly in the extratropical oceans.

scholarly journals Stepped Coastal Water Warming Revealed by Multiparametric Monitoring at NW Mediterranean Fixed Stations

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2658
Author(s):  
Nixon Bahamon ◽  
Jacopo Aguzzi ◽  
Miguel Ángel Ahumada-Sempoal ◽  
Raffaele Bernardello ◽  
Charlotte Reuschel ◽  
...  
Keyword(s):  
Global Warming ◽  
Mediterranean Sea ◽  
Water Column ◽  
Mixed Layer Depth ◽  
Submarine Canyon ◽  
Warming Trend ◽  
Coastal Ecosystems ◽  
Heat Fluxes ◽  
Global Ocean ◽  
Surface Heat Fluxes

Since 2014, the global land and sea surface temperature has scaled 0.23 °C above the decadal average (2009–2018). Reports indicate that Mediterranean Sea temperatures have been rising at faster rates than in the global ocean. Oceanographic time series of physical and biogeochemical data collected from an onboard and a multisensor mooring array in the northwestern Mediterranean Sea (Blanes submarine canyon, Balearic Sea) during 2009–2018 revealed an abrupt temperature rising since 2014, in line with regional and global warming. Since 2014, the oligotrophic conditions of the water column have intensified, with temperature increasing 0.61 °C on the surface and 0.47 °C in the whole water column in continental shelf waters. Water transparency has increased due to a decrease in turbidity anomaly of −0.1 FTU. Since 2013, inshore chlorophyll a concentration remained below the average (−0.15 mg·l−1) and silicates showed a declining trend. The mixed layer depth showed deepening in winter and remained steady in summer. The net surface heat fluxes did not show any trend linked to the local warming, probably due to the influence of incoming offshore waters produced by the interaction between the Northern Current and the submarine canyon. Present regional and global water heating pattern is increasing the stress of highly diverse coastal ecosystems at unprecedented levels, as reported by the literature. The strengthening of the oligotrophic conditions in the study area may also apply as a cautionary warning to similar coastal ecosystems around the world following the global warming trend.

scholarly journals Sources of Uncertainty in Future Projections of the Carbon Cycle

2016 ◽  
Vol 29 (20) ◽  
pp. 7203-7213 ◽  
Author(s):  
Alan J. Hewitt ◽  
Ben B. B. Booth ◽  
Chris D. Jones ◽  
Eddy S. Robertson ◽  
Andy J. Wiltshire ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Carbon Fluxes ◽  
Global Ocean ◽  
Lead Times ◽  
Carbon Uptake ◽  
The North ◽  
Ocean Carbon

Abstract The inclusion of carbon cycle processes within CMIP5 Earth system models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way analysis of variance (ANOVA) approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between representative concentration pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modeled processes that determine ocean fluxes are currently better constrained than those of land fluxes; thus, one can be more confident in linking different future socioeconomic pathways to consequences of ocean carbon uptake than for land carbon uptake. The contribution of internal variance is negligible for ocean fluxes and small for land fluxes, indicating that there is little dependence on the initial conditions. The apparent agreement in atmosphere–ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modeled processes represent an important source of variability in projected regional fluxes.

Global warming and forest fires in Canada

1993 ◽  
Vol 69 (3) ◽  
pp. 290-293 ◽  
Author(s):  
Brian J. Stocks
Keyword(s):  
Global Warming ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Management ◽  
Warming Trend ◽  
Research Activities ◽  
Forest Fire Management ◽  
The Future ◽  
Innovative Thinking ◽  
The Impact

The looming possibility of global warming raises legitimate concerns for the future of the forest resource in Canada. While evidence of a global warming trend is not conclusive at this time, governments would be wise to anticipate, and begin planning for, such an eventuality. The forest fire business is likely to be affected both early and dramatically by any trend toward warmer and drier conditions in Canada, and fire managers should be aware that the future will likely require new and innovative thinking in forest fire management. This paper summarizes research activities currently underway to assess the impact of global warming on forest fires, and speculates on future fire management problems and strategies.

scholarly journals Validation of an ocean shelf model for the prediction of mixed-layer properties in the Mediterranean Sea west of Sardinia

Ocean Science ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 235-257 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Boundary Conditions ◽  
Mixed Layer ◽  
Initial Conditions ◽  
Mixed Layer Depth ◽  
Vertical Mixing ◽  
Global Ocean ◽  
Wave Number ◽  
Surface Boundary ◽  
Regional Ocean Modeling System ◽  
The Mediterranean

Abstract. The Regional Ocean Modeling System (ROMS) has been employed to explore the sensitivity of the forecast skill of mixed-layer properties to initial conditions, boundary conditions, and vertical mixing parameterisations. The initial and lateral boundary conditions were provided by the Mediterranean Forecasting System (MFS) or by the MERCATOR global ocean circulation model via one-way nesting; the initial conditions were additionally updated through the assimilation of observations. Nowcasts and forecasts from the weather forecast models COSMO-ME and COSMO-IT, partly melded with observations, served as surface boundary conditions. The vertical mixing was parameterised by the GLS (generic length scale) scheme Umlauf and Burchard (2003) in four different set-ups. All ROMS forecasts were validated against the observations which were taken during the REP14-MED survey to the west of Sardinia. Nesting ROMS in MERCATOR and updating the initial conditions through data assimilation provided the best agreement of the predicted mixed-layer properties with the time series from a moored thermistor chain. Further improvement was obtained by the usage of COSMO-ME atmospheric forcing, which was melded with real observations, and by the application of the k-ω vertical mixing scheme with increased vertical eddy diffusivity. The predicted temporal variability of the mixed-layer temperature was reasonably well correlated with the observed variability, while the modelled variability of the mixed-layer depth exhibited only agreement with the observations near the diurnal frequency peak. For the forecasted horizontal variability, reasonable agreement was found with observations from a ScanFish section, but only for the mesoscale wave number band; the observed sub-mesoscale variability was not reproduced by ROMS.

Global warming trend?

Eos ◽  
1976 ◽  
Vol 57 (9) ◽  
pp. 631
Author(s):  
Anonymous
Keyword(s):  
Global Warming ◽  
Warming Trend

Revisiting the existence of the global warming slowdown during the early 21st century

2021 ◽  
pp. 1-40
Keyword(s):  
Global Warming ◽  
Greenhouse Gas ◽  
Selection Bias ◽  
21St Century ◽  
Linear Trend ◽  
Warming Trend ◽  
Short Term ◽  
Early 21St Century ◽  
Decadal Scale ◽  
Global Temperature Change

Abstract There are heated debates on the existence of the global warming slowdown during the early 21st century. Although efforts have been made to clarify or reconcile the controversy over the issue, it is not explicitly addressed, restricting the understanding of global temperature change particularly under the background of increasing greenhouse-gas concentrations. Here, using extensive temperature datasets, we comprehensively reexamine the existence of the slowdown under all existing definitions during all decadal-scale periods spanning 1990-2017. Results show that the short-term linear-trend dependent definitions of slowdown make its identification severely suffer from the period selection bias, which largely explains the controversy over its existence. Also, the controversy is further aggravated by the significant impacts of the differences between various datasets on the recent temperature trend and the different baselines for measuring slowdown prescribed by various definitions. However, when the focus is shifted from specific periods to the probability of slowdown events, we find the probability is significantly higher in the 2000s than in the 1990s, regardless of which definition and dataset are adopted. This supports a slowdown during the early 21st century relative to the warming surge in the late 20th century, despite higher greenhouse-gas concentrations. Furthermore, we demonstrate that this decadal-scale slowdown is not incompatible with the centennial-scale anthropogenic warming trend, which has been accelerating since 1850 and never pauses or slows. This work partly reconciles the controversy over the existence of the warming slowdown and the discrepancy between the slowdown and anthropogenic warming.

Introduction

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Physical System ◽  
Weather Prediction ◽  
Climate Prediction ◽  
Lead Times ◽  
Prediction Methods ◽  
Data Sets ◽  
Short Term ◽  
Global Data Sets ◽  
Geophysical System

This is first and foremost a book about short-term climate prediction. The predictions we have in mind are for weather/climate elements, mainly temperature (T) and precipitation (P), at lead times longer than two weeks, beyond the realm of detailed Numerical Weather Prediction (NWP), i.e. predictions for the next month and the next seasons out to at most a few years. call this short-term climate so as to distinguish it from long-term climate change which is not the main subject of this book. A few decades ago “short-term climate prediction” was known as “longrange weather prediction”. In order to understand short-term climate predictions, their skill and what they reveal about the atmosphere, ocean and land, several chapters are devoted to constructing prediction methods. The approach taken is mainly empirical, which means literally that it is based in experience. We will use global data sets to represent the climate and weather humanity experienced (and measured!) in the past several decades. The idea is to use these existing data sets in order to construct prediction methods. In doing so we want to acknowledge that every measurement (with error bars) is a monument about the workings of Nature. We thought about using the word “statistical” instead of “empirical” in the title of the book. These two notions overlap, obviously, but we prefer the word “empirical” because we are driven more by intuition than by a desire to apply existing or developing new statistical theory. While constructing prediction methods we want to discover to the greatest extent possible how the physical system works from observations. While not mentioned in the title, diagnostics of the physical system will thus be an important part of the book as well. We use a variety of classical tools to diagnose the geophysical system. Some of these tools have been developed further and/or old tools are applied in novel ways. We do not intend to cover all diagnostics methods, only those that relate closely to prediction. There will be an emphasis on methods used in operational prediction. It is quite difficult to gain a comprehensive idea from existing literature about methods used in operational short-term climate prediction.

scholarly journals Global vegetation variability and its response to elevated CO<sub>2</sub>, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data

2019 ◽  
Vol 10 (1) ◽  
pp. 9-29 ◽  
Author(s):  
Ye Liu ◽  
Yongkang Xue ◽  
Glen MacDonald ◽  
Peter Cox ◽  
Zhengqiu Zhang
Keyword(s):  
Global Warming ◽  
Climate Variability ◽  
Environmental Drivers ◽  
Arid Areas ◽  
Area Index ◽  
Substantial Impact ◽  
Positive Effects ◽  
Vegetation Fraction ◽  
Before And After ◽  
Semi Arid

Abstract. The climate regime shift during the 1980s had a substantial impact on the terrestrial ecosystems and vegetation at different scales. However, the mechanisms driving vegetation changes, before and after the shift, remain unclear. In this study, we used a biophysical dynamic vegetation model to estimate large-scale trends in terms of carbon fixation, vegetation growth, and expansion during the period 1958–2007, and to attribute these changes to environmental drivers including elevated atmospheric CO2 concentration (hereafter eCO2), global warming, and climate variability (hereafter CV). Simulated leaf area index (LAI) and gross primary production (GPP) were evaluated against observation-based data. Significant spatial correlations are found (correlations > 0.87), along with regionally varying temporal correlations of 0.34–0.80 for LAI and 0.45–0.83 for GPP. More than 40 % of the global land area shows significant positive (increase) or negative (decrease) trends in LAI and GPP during 1958–2007. Regions over the globe show different characteristics in terms of ecosystem trends before and after the 1980s. While 11.7 % and 19.3 % of land have had consistently positive LAI and GPP trends, respectively, since 1958, 17.1 % and 20.1 % of land saw LAI and GPP trends, respectively, reverse during the 1980s. Vegetation fraction cover (FRAC) trends, representing vegetation expansion and/or shrinking, are found at the edges of semi-arid areas and polar areas. Environmental drivers affect the change in ecosystem trend over different regions. Overall, eCO2 consistently contributes to positive LAI and GPP trends in the tropics. Global warming mostly affects LAI, with positive effects in high latitudes and negative effects in subtropical semi-arid areas. CV is found to dominate the variability of FRAC, LAI, and GPP in the semi-humid and semi-arid areas. The eCO2 and global warming effects increased after the 1980s, while the CV effect reversed during the 1980s. In addition, plant competition is shown to have played an important role in determining which driver dominated the regional trends. This paper presents new insight into ecosystem variability and changes in the varying climate since the 1950s.

Ensembles of global ocean analyses for seasonal climate prediction: impact of temperature assimilation

2005 ◽  
Vol 57 (3) ◽  
pp. 375-386 ◽  
Author(s):  
Philippe Rogel ◽  
Anthony T. Weaver ◽  
Nicolas Daget ◽  
Sophie Ricci ◽  
Eric Machu
Keyword(s):  
Climate Prediction ◽  
Global Ocean ◽  
Seasonal Climate ◽  
Seasonal Climate Prediction
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