scholarly journals Is global warming already changing ocean productivity?

2009 ◽  
Vol 6 (6) ◽  
pp. 10311-10354 ◽  
Author(s):  
S. A. Henson ◽  
J. L. Sarmiento ◽  
J. P. Dunne ◽  
L. Bopp ◽  
I. Lima ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Global Warming ◽  
Primary Production ◽  
Decadal Variability ◽  
Warming Trend ◽  
Natural Variability ◽  
Biogeochemical Models ◽  
Ocean Productivity ◽  
The Impact

Abstract. Global warming is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on the global distribution of ocean productivity comes from satellite ocean colour data. Now that over ten years of SeaWiFS data have accumulated, can we begin to detect and attribute global warming trends in productivity? Here we compare recent trends in SeaWiFS data to longer-term records from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of real trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global warming. Instead, our analyses suggest that a time series of ~40 yr length is needed to distinguish a global warming trend from natural variability. Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the global warming trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

scholarly journals Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

2010 ◽  
Vol 7 (2) ◽  
pp. 621-640 ◽  
Author(s):  
S. A. Henson ◽  
J. L. Sarmiento ◽  
J. P. Dunne ◽  
L. Bopp ◽  
I. Lima ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Primary Production ◽  
Global Climate Change ◽  
Decadal Variability ◽  
Global Climate ◽  
Warming Trend ◽  
Ocean Colour ◽  
Ocean Productivity ◽  
The Impact

Abstract. Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

scholarly journals The impact of global warming on seasonality of ocean primary production

2013 ◽  
Vol 10 (1) ◽  
pp. 1421-1450 ◽  
Author(s):  
S. Henson ◽  
H. Cole ◽  
C. Beaulieu ◽  
A. Yool
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Nitrate Concentration ◽  
Mixed Layer Depth ◽  
Climate Change Impacts ◽  
The Arctic ◽  
Layer Depth ◽  
Biogeochemical Models ◽  
Ocean Productivity ◽  
The Impact

Abstract. The seasonal cycle (i.e. phenology) of oceanic primary production (PP) is expected to change in response to climate warming. Here, we use output from 6 global biogeochemical models to examine the response in the seasonal amplitude of PP and timing of peak PP to the IPCC AR5 warming scenario. We also investigate whether trends in PP phenology may be more rapidly detectable than trends in PP itself. The seasonal amplitude of PP decreases by an average of 1–2% per year by 2100 in most biomes, with the exception of the Arctic which sees an increase of ~1% per year. This is accompanied by an advance in the timing of peak PP by ~0.5–1 months by 2100 over much of the globe, and particularly pronounced in the Arctic. These changes are driven by an increase in seasonal amplitude of sea surface temperature (where the maxima get hotter faster than the minima) and a decrease in the seasonal amplitude of the mixed layer depth and surface nitrate concentration. Our results indicate a transformation of currently strongly seasonal (bloom forming) regions, typically found at high latitudes, into weakly seasonal (non-bloom) regions, characteristic of contemporary subtropical conditions. On average, 36 yr of data are needed to detect a climate change-driven trend in the seasonal amplitude of PP, compared to 32 yr for mean annual PP. We conclude that analysis of phytoplankton phenology is not necessarily a shortcut to detecting climate change impacts on ocean productivity.

scholarly journals The impact of global warming on seasonality of ocean primary production

2013 ◽  
Vol 10 (6) ◽  
pp. 4357-4369 ◽  
Author(s):  
S. Henson ◽  
H. Cole ◽  
C. Beaulieu ◽  
A. Yool
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Mixed Layer Depth ◽  
Climate Change Impacts ◽  
The Arctic ◽  
Biogeochemical Models ◽  
Ocean Productivity ◽  
Phenological Changes ◽  
Resolution Model ◽  
The Impact

Abstract. The seasonal cycle (i.e. phenology) of oceanic primary production (PP) is expected to change in response to climate warming. Here, we use output from 6 global biogeochemical models to examine the response in the seasonal amplitude of PP and timing of peak PP to the IPCC AR5 warming scenario. We also investigate whether trends in PP phenology may be more rapidly detectable than trends in annual mean PP. The seasonal amplitude of PP decreases by an average of 1–2% per year by 2100 in most biomes, with the exception of the Arctic which sees an increase of ~1% per year. This is accompanied by an advance in the timing of peak PP by ~0.5–1 months by 2100 over much of the globe, and particularly pronounced in the Arctic. These changes are driven by an increase in seasonal amplitude of sea surface temperature (where the maxima get hotter faster than the minima) and a decrease in the seasonal amplitude of the mixed layer depth and surface nitrate concentration. Our results indicate a transformation of currently strongly seasonal (bloom forming) regions, typically found at high latitudes, into weakly seasonal (non-bloom) regions, characteristic of contemporary subtropical conditions. On average, 36 yr of data are needed to detect a climate-change-driven trend in the seasonal amplitude of PP, compared to 32 yr for mean annual PP. Monthly resolution model output is found to be inadequate for resolving phenological changes. We conclude that analysis of phytoplankton seasonality is not necessarily a shortcut to detecting climate change impacts on ocean productivity.

scholarly journals The analysis of the relationship between CO2 level and economic growth

2021 ◽  
pp. 24-28
Author(s):  
Zoltán Szira ◽  
Bárdos Kinga Ilona ◽  
Hani Alghamdi ◽  
Tumentsetseg Enkhjav ◽  
Erika Varga
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Developing Countries ◽  
Global Warming ◽  
Developed Countries ◽  
Warming Trend ◽  
Carbon Abatement ◽  
The Impact ◽  
The Relationship

2019 was Earth's second warmest year since 1850. In 2019 the global mean temperature was cooler than in 2016, but warmer than any other year explicitly measured. Consequently, 2016 is still the warmest year in historical observation history. Year-to-year rankings are likely to reflect natural fluctuations in the short term, but the overall pattern remains consistent with a long-term global warming trend. This would be predicted from global warming, caused by greenhouse gases, temperature increase across the globe is broadly spread, impacting almost all areas of land and oceans. “Climate change" and "global warming" are often used interchangeably, but are of distinct significance. Global warming is the long-term heating of the Earth's climate system, observed since the pre-industrial period as a result of human activities, mainly the combustion of fossil fuel, which raises the heat-trapping greenhouse gas levels in the Earth's air. The term is often used interchangeably with the term climate change, as the latter applies to warming, caused both humanly and naturally, and the impact it has on our planet. This is most generally calculated as the average increase in global surface temperature on Earth. In our research, we examine the relationship between the regulation of carbon emissions and the GDP / capita relationship between developed and developing countries. We assumed applying carbon abatement policies will reduce economic growth and GDP in developed countries, but it will rise economic growth and GDP in developing countries.

scholarly journals How Would the Twenty-First-Century Warming Influence Pacific Decadal Variability and Its Connection to North American Rainfall: Assessment Based on a Revised Procedure for the IPO/PDO

2018 ◽  
Vol 31 (4) ◽  
pp. 1547-1563 ◽  
Author(s):  
Yangyang Xu ◽  
Aixue Hu
Keyword(s):  
Time Series ◽  
Global Warming ◽  
North American ◽  
Decadal Variability ◽  
Warming Trend ◽  
Vital Role ◽  
First Century ◽  
Eof Analysis ◽  
Decadal Climate Variability ◽  
Twenty First Century

Decadal climate variability of sea surface temperature (SST) over the Pacific Ocean can be characterized by interdecadal Pacific oscillation (IPO) or Pacific decadal oscillation (PDO) based on empirical orthogonal function (EOF) analysis. Although the procedures to derive the IPO and PDO indices differ in their regional focuses and filtering methods to remove interannual variability, the IPO and PDO are highly correlated in time and are often used interchangeably. Studies have shown that the IPO and PDO conjointly (IPO/PDO for conciseness) play a vital role in modulating the pace of global warming. It is less clear, however, how externally forced global warming may, in turn, affect the IPO/PDO. One obstacle to revealing this effect is that the conventional definitions of the IPO/PDO fail to account for the spatial heterogeneity of the background warming trend, which causes the IPO/PDO to be conflated with the warming trend, especially for the twenty-first-century simulation when the forced change is likely to be more dominant. Using a large-ensemble simulation in the Community Earth System Model, version 1 (CESM1), it is shown here that a better practice of detrending prior to EOF analysis is to remove the local and nonlinear trend, defined as the ensemble-mean time series at each grid box (or simply as the quadratic fit of the local time series if such an ensemble is not available). The revised IPO/PDO index is purely indicative of internal decadal variability. In the twenty-first-century warmer climate, the IPO/PDO has a weaker amplitude in space, a higher frequency in time, and a muted impact on global and North American temperature and rainfall.

Evaluation of the daily cycle in the simulation of the ClimEx large-ensemble

2021 ◽  
Author(s):  
Anne-Marie Begin
Keyword(s):  
Climate Change ◽  
Estimation Error ◽  
Natural Variability ◽  
Extreme Weather Events ◽  
Large Ensemble ◽  
Daily Cycle ◽  
Minimum Number ◽  
The Mean ◽  
Mean Cycle ◽  
The Impact

<p>To estimate the impact of climate change on our society we need to use climate projections based on numerical models. These models make it possible to assess the effects on climate of the increase in greenhouse gases (GHG) as well as natural variability. We know that the global average temperature will increase and that the occurrence, intensity and spatio-temporal distribution of extreme precipitations will change. These extreme weather events cause droughts, floods and other natural disasters that have significant consequences on our life and environment. Precipitation is a key variable in adapting to climate change.</p><p> </p><p>This study focuses on the ClimEx large ensemble, a set of 50 independent simulations created to study the effect of climate change and natural variability on the water network in Quebec. This dataset consists of simulations produced using the Canadian Regional Climate Model version 5 (CRCM5) at 12 km of resolution driven by simulations from the second generation Canadian Earth System Model (CanESM2) global model at 310 km of resolution.</p><p> </p><p>The aim of the project is to evaluate the performance of the ClimEx ensemble in simulating the daily cycle and representing extreme values.  To get there, 30 years of hourly time series for precipitation and 3 hourly for temperature are analyzed. The simulations are compared with the values from the simulation of CRCM5 driven by ERA-Interim reanalysis, the ERA5 reanalysis and Environment and Climate Change Canada (ECCC) stations. An evaluation of the sensitivity of different statistics to the number of members is also performed.</p><p> </p><p>The daily cycle of precipitation from ClimEx shows mainly non-significant correlations with the other datasets and its amplitude is less than the observation datas from ECCC stations. For temperature, the correlation is strong and the amplitude of the cycle is similar to observations. ClimEx provides a fairly good representation of the 95, 97, 99<sup>th</sup> quantiles for precipitation. For temperature it represents a good distribution of quantiles but with a warm bias in southern Quebec. For precipitation hourly maximum, ClimEx shows values 10 times higher than ERA5.  For temperature, minimum and maximum values may exceed the ERA5 limit by up to 20°C. For precipitation, the minimum number of members for the estimation of the 95 and 99<sup>th</sup><sup></sup>quantiles and the mean cycle is between 15 and 50 for an estimation error of less than 5%. For the 95, 99<sup>th</sup> quantiles of temperature, the minimum number of members is between 1 and 17 and for the mean cycle 1 to 2 members are necessary to obtain an estimation error of less than 0.5°C.</p>

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 Regional variability of acidification in the Arctic: a sea of contrasts

2014 ◽  
Vol 11 (2) ◽  
pp. 293-308 ◽  
Author(s):  
E. E. Popova ◽  
A. Yool ◽  
Y. Aksenov ◽  
A. C. Coward ◽  
T. R. Anderson
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Arctic Ocean ◽  
Canadian Arctic ◽  
Atmospheric Co2 ◽  
The Arctic ◽  
Canadian Arctic Archipelago ◽  
Regional Variability ◽  
The Arctic Ocean ◽  
The Impact

Abstract. The Arctic Ocean is a region that is particularly vulnerable to the impact of ocean acidification driven by rising atmospheric CO2, with potentially negative consequences for calcifying organisms such as coccolithophorids and foraminiferans. In this study, we use an ocean-only general circulation model, with embedded biogeochemistry and a comprehensive description of the ocean carbon cycle, to study the response of pH and saturation states of calcite and aragonite to rising atmospheric pCO2 and changing climate in the Arctic Ocean. Particular attention is paid to the strong regional variability within the Arctic, and, for comparison, simulation results are contrasted with those for the global ocean. Simulations were run to year 2099 using the RCP8.5 (an Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) scenario with the highest concentrations of atmospheric CO2). The separate impacts of the direct increase in atmospheric CO2 and indirect effects via impact of climate change (changing temperature, stratification, primary production and freshwater fluxes) were examined by undertaking two simulations, one with the full system and the other in which atmospheric CO2 was prevented from increasing beyond its preindustrial level (year 1860). Results indicate that the impact of climate change, and spatial heterogeneity thereof, plays a strong role in the declines in pH and carbonate saturation (Ω) seen in the Arctic. The central Arctic, Canadian Arctic Archipelago and Baffin Bay show greatest rates of acidification and Ω decline as a result of melting sea ice. In contrast, areas affected by Atlantic inflow including the Greenland Sea and outer shelves of the Barents, Kara and Laptev seas, had minimal decreases in pH and Ω because diminishing ice cover led to greater vertical mixing and primary production. As a consequence, the projected onset of undersaturation in respect to aragonite is highly variable regionally within the Arctic, occurring during the decade of 2000–2010 in the Siberian shelves and Canadian Arctic Archipelago, but as late as the 2080s in the Barents and Norwegian seas. We conclude that, for future projections of acidification and carbonate saturation state in the Arctic, regional variability is significant and needs to be adequately resolved, with particular emphasis on reliable projections of the rates of retreat of the sea ice, which are a major source of uncertainty.

scholarly journals Desertification in Forest, Range and Desert Landuses of Tehran Province, Under the Impact of Climate Change

2016 ◽  
Author(s):  
Hadi Eskandari Dameneh ◽  
Moslem Borji ◽  
Hassan Khosravi ◽  
Ali Salajeghe
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Net Primary Production ◽  
Degradation Process ◽  
Early Warning Systems ◽  
Land Uses ◽  
Emission Scenarios ◽  
Desert Ecosystems ◽  
Tehran Province ◽  
The Impact

Abstract. Persistence of widespread degradation in arid and semi-arid region of Iran necessitates using of monitoring and evaluation systems with appropriate accuracy to determine the degradation process and adoption of early warning systems; because after transition from some thresholds, effective reversible function of ecosystems will not be very easy. This paper tries to monitor the degradation and desertification trends in three land uses including range, forest and desert lands affected by climate change in Tehran province for 2000s and 2030s. For assessing climate changes of Mehrabad synoptic stations the data of two emission scenarios including A2 and B2 were used using statistical downscaling techniques and data generated by SDSM model. The index of net primary production resulting from MODIS satellite images was employed as an indicator of destruction from 2001 to 2010. The results showed that temperature is the most effective driver force which alters the net primary production in rangeland, forest and desert ecosystems of Tehran province. On the basis of monitoring findings under real conditions, in the 2000s, over 60 % of rangelands and 80 % of the forests have been below the average production in the province. On the other hand, the long-term average changes of NPP in rangeland and forests indicated the presence of relatively large areas of these land uses with production rate lower than the desert. The results also showed that, assuming the existence of circumstances of each emission scenarios, the desertification status will not improve significantly in the rangelands and forests of Tehran province.

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