scholarly journals Climate change and decadal shifts in the phenology of larval fishes in the California Current ecosystem

2015 ◽  
Vol 112 (30) ◽  
pp. E4065-E4074 ◽  
Author(s):  
Rebecca G. Asch
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Coastal Upwelling ◽  
Southern Oscillation ◽  
Larval Abundance ◽  
Boundary Current ◽  
Intergovernmental Panel ◽  
Change Models ◽  
Larval Fishes ◽  
Decadal Climate

Climate change has prompted an earlier arrival of spring in numerous ecosystems. It is uncertain whether such changes are occurring in Eastern Boundary Current Upwelling ecosystems, because these regions are subject to natural decadal climate variability, and regional climate models predict seasonal delays in upwelling. To answer this question, the phenology of 43 species of larval fishes was investigated between 1951 and 2008 off southern California. Ordination of the fish community showed earlier phenological progression in more recent years. Thirty-nine percent of seasonal peaks in larval abundance occurred earlier in the year, whereas 18% were delayed. The species whose phenology became earlier were characterized by an offshore, pelagic distribution, whereas species with delayed phenology were more likely to reside in coastal, demersal habitats. Phenological changes were more closely associated with a trend toward earlier warming of surface waters rather than decadal climate cycles, such as the Pacific Decadal Oscillation and North Pacific Gyre Oscillation. Species with long-term advances and delays in phenology reacted similarly to warming at the interannual time scale as demonstrated by responses to the El Niño Southern Oscillation. The trend toward earlier spawning was correlated with changes in sea surface temperature (SST) and mesozooplankton displacement volume, but not coastal upwelling. SST and upwelling were correlated with delays in fish phenology. For species with 20th century advances in phenology, future projections indicate that current trends will continue unabated. The fate of species with delayed phenology is less clear due to differences between Intergovernmental Panel on Climate Change models in projected upwelling trends.

scholarly journals Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR

2013 ◽  
Vol 10 (6) ◽  
pp. 3917-3930 ◽  
Author(s):  
J. Jubanski ◽  
U. Ballhorn ◽  
K. Kronseder ◽  
F. Siegert ◽  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Forest Type ◽  
Airborne Lidar ◽  
Above Ground Biomass ◽  
Inventory Data ◽  
Intergovernmental Panel ◽  
Lowland Forest ◽  
Change Models ◽  
Ground Biomass

Abstract. Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

scholarly journals Detection of large above ground biomass variability in lowland forest ecosystems by airborne LiDAR

2012 ◽  
Vol 9 (8) ◽  
pp. 11815-11842 ◽  
Author(s):  
J. Jubanski ◽  
U. Ballhorn ◽  
K. Kronseder ◽  
J. Franke ◽  
F. Siegert
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Forest Type ◽  
Airborne Lidar ◽  
Above Ground Biomass ◽  
Inventory Data ◽  
Intergovernmental Panel ◽  
Lowland Forest ◽  
Change Models ◽  
Ground Biomass

Abstract. Quantification of tropical forest Above Ground Biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne Light Detection and Ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square meter of 2–4 resulted in the best cost-benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 46%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong GHG emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

scholarly journals Global reconstruction reduces the uncertainty of oceanic nitrous oxide emissions and reveals a vigorous seasonal cycle

2020 ◽  
Vol 117 (22) ◽  
pp. 11954-11960 ◽  
Author(s):  
Simon Yang ◽  
Bonnie X. Chang ◽  
Mark J. Warner ◽  
Thomas S. Weber ◽  
Annie M. Bourbonnais ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Seasonal Cycle ◽  
Coastal Upwelling ◽  
Learning Algorithm ◽  
Southern Oscillation ◽  
Latitudinal Gradients ◽  
Nitrous Oxide Emissions ◽  
Boreal Summer ◽  
Intergovernmental Panel ◽  
Tropical Ocean

Assessment of the global budget of the greenhouse gas nitrous oxide (N2O) is limited by poor knowledge of the oceanicN2O flux to the atmosphere, of which the magnitude, spatial distribution, and temporal variability remain highly uncertain. Here, we reconstruct climatologicalN2O emissions from the ocean by training a supervised learning algorithm with over 158,000N2O measurements from the surface ocean—the largest synthesis to date. The reconstruction captures observed latitudinal gradients and coastal hot spots ofN2O flux and reveals a vigorous global seasonal cycle. We estimate an annual meanN2O flux of 4.2 ± 1.0 Tg N⋅y−1, 64% of which occurs in the tropics, and 20% in coastal upwelling systems that occupy less than 3% of the ocean area. ThisN2O flux ranges from a low of 3.3 ± 1.3 Tg N⋅y−1in the boreal spring to a high of 5.5 ± 2.0 Tg N⋅y−1in the boreal summer. Much of the seasonal variations in globalN2O emissions can be traced to seasonal upwelling in the tropical ocean and winter mixing in the Southern Ocean. The dominant contribution to seasonality by productive, low-oxygen tropical upwelling systems (>75%) suggests a sensitivity of the globalN2O flux to El Niño–Southern Oscillation and anthropogenic stratification of the low latitude ocean. This ocean flux estimate is consistent with the range adopted by the Intergovernmental Panel on Climate Change, but reduces its uncertainty by more than fivefold, enabling more precise determination of other terms in the atmosphericN2O budget.

scholarly journals The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale

2018 ◽  
Vol 99 (11) ◽  
pp. 2341-2359 ◽  
Author(s):  
M. J. Roberts ◽  
P. L. Vidale ◽  
C. Senior ◽  
H. T. Hewitt ◽  
C. Bates ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Time Scales ◽  
Climate Models ◽  
Water Cycle ◽  
Regional Scale ◽  
Coupled Model ◽  
Convective Precipitation ◽  
Climate Simulation ◽  
Intergovernmental Panel

AbstractThe time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms).

scholarly journals Atmospheric Blocking and Mean Biases in Climate Models

2010 ◽  
Vol 23 (23) ◽  
pp. 6143-6152 ◽  
Author(s):  
Adam A. Scaife ◽  
Tim Woollings ◽  
Jeff Knight ◽  
Gill Martin ◽  
Tim Hinton
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Model Data ◽  
Atmospheric Blocking ◽  
Intergovernmental Panel ◽  
Weather And Climate ◽  
Main Culprit

Abstract Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

scholarly journals An Overview of CMIP5 and the Experiment Design

2012 ◽  
Vol 93 (4) ◽  
pp. 485-498 ◽  
Author(s):  
Karl E. Taylor ◽  
Ronald J. Stouffer ◽  
Gerald A. Meehl
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Experiment Design ◽  
Global Climate Models ◽  
Model Output ◽  
Earth System ◽  
Intergovernmental Panel ◽  
System Models ◽  
Earth System Models

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.

Climate change and wind intensification in coastal upwelling ecosystems

Science ◽  
2014 ◽  
Vol 345 (6192) ◽  
pp. 77-80 ◽  
Author(s):  
W. J. Sydeman ◽  
M. García-Reyes ◽  
D. S. Schoeman ◽  
R. R. Rykaczewski ◽  
S. A. Thompson ◽  
...  
Keyword(s):  
Climate Change ◽  
Coastal Upwelling ◽  
Meta Analysis ◽  
Boundary Current ◽  
Eastern Boundary ◽  
Warming Pattern ◽  
Eastern Boundary Current ◽  
Coastal Winds ◽  
Variable Support ◽  
Current Systems

In 1990, Andrew Bakun proposed that increasing greenhouse gas concentrations would force intensification of upwelling-favorable winds in eastern boundary current systems that contribute substantial services to society. Because there is considerable disagreement about whether contemporary wind trends support Bakun’s hypothesis, we performed a meta-analysis of the literature on upwelling-favorable wind intensification. The preponderance of published analyses suggests that winds have intensified in the California, Benguela, and Humboldt upwelling systems and weakened in the Iberian system over time scales ranging up to 60 years; wind change is equivocal in the Canary system. Stronger intensification signals are observed at higher latitudes, consistent with the warming pattern associated with climate change. Overall, reported changes in coastal winds, although subtle and spatially variable, support Bakun’s hypothesis of upwelling intensification in eastern boundary current systems.

The challenge of combining initialised and uninitialised decadal projections

2020 ◽  
Author(s):  
James Murphy
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Change Scenarios ◽  
Internal Climate Variability ◽  
Climate Risks ◽  
Early Results ◽  
Ensemble Mean ◽  
Decadal Climate

<p>The challenge of combining initialised and uninitialised decadal projections</p><p>James Murphy, Robin Clark, Nick Dunstone, Glen Harris, Leon Hermanson and Doug Smith</p><p>During the past 10 years or so, exploratory work in initialised decadal climate prediction, using global climate models started from recent analyses of observations, has grown into a coordinated international programme that contributes to IPCC assessments. At the same time, countries have continued to develop and update their national climate change scenarios.  These typically cover the full 21<sup>st</sup> century, including the initial decade that overlaps with the latest initialised forecasts. To date, however, national scenarios continue to be based exclusively on long-term (uninitialised) climate change simulations, with initialised information regarded as a separate stream of information.</p><p>We will use early results from the latest UK national scenarios (UKCP), and the latest CMIP6 initialised predictions, to illustrate the potential and challenges associated with the notion of combining both streams of information. This involves assessing the effects of initialisation on predictability and uncertainty (as indicated, for example, by the skill of ensemble-mean forecasts and the spread amongst constituent ensemble members). Here, a particular challenge involves interpretation of the “signal-to-noise” problem, in which ensemble-mean skill can sometimes be found which is larger than would be expected on the basis of the ensemble spread. In addition to initialisation, we will also emphasise the importance of understanding how the assessment of climate risks depends on other features of prediction system design, including the sampling of model uncertainties and the simulation of internal climate variability.</p>

Timothy yield and nutritive value with a three-harvest system under the projected future climate in Canada

2014 ◽  
Vol 94 (2) ◽  
pp. 213-222 ◽  
Author(s):  
Qi Jing ◽  
Gilles Bélanger ◽  
Budong Qian ◽  
Vern Baron
Keyword(s):  
Climate Change ◽  
Nutritive Value ◽  
Climate Models ◽  
Phleum Pratense ◽  
Global Climate Models ◽  
Future Climate ◽  
Climate Scenarios ◽  
Intergovernmental Panel ◽  
Stochastic Weather Generator ◽  
The Impact

Jing, Q., Bélanger, G., Qian, B. and Baron, V. 2014. Timothy yield and nutritive value with a three-harvest system under the projected future climate in Canada. Can. J. Plant Sci. 94: 213–222. Timothy (Phleum pratense L.) is harvested twice annually in Canada but with projected climate change, an additional harvest may be possible. Our objective was to evaluate the impact on timothy dry matter (DM) yield and key nutritive value attributes of shifting from a two- to a three-harvest system under projected future climate conditions at 10 sites across Canada. Future climate scenarios were generated with a stochastic weather generator (AAFC-WG) using two global climate models under the forcing of two Intergovernmental Panel on Climate Change emission scenarios and, then, used by the CATIMO (Canadian Timothy Model) grass model to simulate DM yield and key nutritive value attributes. Under future climate scenarios (2040–2069), the additional harvest and the resulting three-harvest system are expected to increase annual DM yield (+0.46 to +2.47 Mg DM ha−1) compared with a two-harvest system across Canada but the yield increment will on average be greater in eastern Canada (1.88 Mg DM ha−1) and Agassiz (2.02 Mg DM ha−1) than in the prairie provinces of Canada (0.84 Mg DM ha−1). The DM yield of the first harvest in a three-harvest system is expected to be less than in the two-harvest system, while that of the second harvest would be greater. Decreases in average neutral detergent fibre (NDF) concentration (−19 g kg−1 DM) and digestibility (dNDF, −5 g kg−1 NDF) are also expected with the three-harvest system under future conditions. Our results indicate that timothy will take advantage of projected climate change, through taking a third harvest, thereby increasing annual DM production.

ENSO Model Validation Using Wavelet Probability Analysis

2010 ◽  
Vol 23 (20) ◽  
pp. 5540-5547 ◽  
Author(s):  
Samantha Stevenson ◽  
Baylor Fox-Kemper ◽  
Markus Jochum ◽  
Balaji Rajagopalan ◽  
Stephen G. Yeager
Keyword(s):  
Climate Models ◽  
Robust Statistics ◽  
Model Simulation ◽  
Southern Oscillation ◽  
Probability Distributions ◽  
Wavelet Spectrum ◽  
Exponential Dependence ◽  
Exponential Relationship ◽  
Intergovernmental Panel ◽  
Enso Variability

Abstract A new method to quantify changes in El Niño–Southern Oscillation (ENSO) variability is presented, using the overlap between probability distributions of the wavelet spectrum as measured by the wavelet probability index (WPI). Examples are provided using long integrations of three coupled climate models. When subsets of Niño-3.4 time series are compared, the width of the confidence interval on WPI has an exponential dependence on the length of the subset used, with a statistically identical slope for all three models. This exponential relationship describes the rate at which the system converges toward equilibrium and may be used to determine the necessary simulation length for robust statistics. For the three models tested, a minimum of 250 model years is required to obtain 90% convergence for Niño-3.4, longer than typical Intergovernmental Panel on Climate Change (IPCC) simulations. Applying the same decay relationship to observational data indicates that measuring ENSO variability with 90% confidence requires approximately 240 years of observations, which is substantially longer than the modern SST record. Applying hypothesis testing techniques to the WPI distributions from model subsets and from comparisons of model subsets to the historical Niño-3.4 index then allows statistically robust comparisons of relative model agreement with appropriate confidence levels given the length of the data record and model simulation.

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