Seasonal forecasting of tuna habitat for dynamic spatial management

2011 ◽  
Vol 68 (5) ◽  
pp. 898-911 ◽  
Author(s):  
Alistair J. Hobday ◽  
Jason R. Hartog ◽  
Claire M. Spillman ◽  
Oscar Alves
Keyword(s):  
Fishing Effort ◽  
Reanalysis Data ◽  
Habitat Model ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ocean Reanalysis ◽  
Ocean Atmosphere ◽  
Southern Bluefin Tuna ◽  
Atmosphere Model ◽  
Habitat Boundaries

Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, Thunnus maccoyii ) are a quota-limited species in a multispecies longline fishery; minimizing capture by nonquota holders is an important management concern. A habitat preference model (conditioned with electronic tag data) coupled with ocean reanalysis data has been used since 2003 to generate real-time predicted maps of SBT distribution (nowcasts). These maps are used by fishery managers to restrict fisher access to areas with high predicted SBT distribution. Here we use the coupled ocean–atmosphere model, POAMA (predictive ocean atmosphere model for Australia), and a habitat model to forecast SBT distribution at lead times of up to 4 months. These forecasts are comparable with nowcasts derived from the operational system, and show skill in predicting SBT habitat boundaries out to lead-times of 3–4 months. For this fishery, seasonal forecasts can provide managers and fishers with valuable insights into future habitat distributions for the upcoming months, to better inform operational decisions.

scholarly journals Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

2015 ◽  
Vol 19 (6) ◽  
pp. 2577-2586 ◽  
Author(s):  
F. Wetterhall ◽  
H. C. Winsemius ◽  
E. Dutra ◽  
M. Werner ◽  
E. Pappenberger
Keyword(s):  
Meteorological Drought ◽  
Reanalysis Data ◽  
Lead Times ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Quantile Mapping ◽  
Dry Spells ◽  
Precipitation Threshold ◽  
Staple Crop

Abstract. The rainfall in southern Africa has a large inter-annual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December–February (DJF) with lead times from 0 to 4 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data, which in turn were corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile mapping as well as post-processed using a precipitation threshold to define a dry day. The results indicate that the forecasts show skill in predicting dry spells in comparison with a climatological ensemble based on previous years. Quantile mapping in combination with a precipitation threshold improved the skill of the forecast. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have the potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.

scholarly journals Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

2014 ◽  
Vol 11 (1) ◽  
pp. 861-888 ◽  
Author(s):  
F. Wetterhall ◽  
H. C. Winsemius ◽  
E. Dutra ◽  
M. Werner ◽  
F. Pappenberger
Keyword(s):  
Meteorological Drought ◽  
Spatial Filter ◽  
Reanalysis Data ◽  
Lead Times ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Dry Spells ◽  
Precipitation Threshold ◽  
Staple Crop

Abstract. The rainfall in Southern Africa has a large interannual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December–February (DJF) with lead times from 1 to 5 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data which in turn was corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile matching as well as post-processed using a precipitation threshold to define a dry day as well as spatial filtering. The results indicate that the forecasts show skill in predicting dry spells in comparison with a "climatological ensemble" based on previous years. Quantile matching in combination with a precipitation threshold improved the skill of the forecast, whereas a spatial filter had no effect. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.

scholarly journals Methods of ensemble prediction for seasonal forecasts with a coupled ocean-atmosphere model

2013 ◽  
Vol 54 ◽  
pp. 361
Author(s):  
Jorgen Segerlund Frederiksen ◽  
Carsten Segerlund Frederiksen ◽  
Stacey Lee Osbrough
Keyword(s):  
Ensemble Prediction ◽  
Seasonal Forecasts ◽  
Ocean Atmosphere ◽  
Atmosphere Model

scholarly journals A regional coupled ocean–atmosphere modeling framework (MITgcm–WRF) using ESMF/NUOPC: description and preliminary results for the Red Sea

2018 ◽  
Author(s):  
Rui Sun ◽  
Aneesh Subramanian ◽  
Art Miller ◽  
Matt Mazloff ◽  
Ibrahim Hoteit ◽  
...  
Keyword(s):  
Red Sea ◽  
System Modeling ◽  
Coupled Model ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Modeling Framework ◽  
Coupled Models ◽  
Ocean Atmosphere ◽  
Atmosphere Model ◽  
Regional Coupled Model

Abstract. A new regional coupled ocean–atmosphere model is developed to study air–sea feedbacks. The coupled model is based on two open-source community model components: (1) MITgcm ocean model; (2) Weather Research and Forecasting (WRF) atmosphere model. The coupling between these components is performed using ESMF (Earth System Modeling Framework) and implemented according to National United Operational Prediction Capability (NUOPC) consortium. The regional coupled model allows affordable simulation where oceanic mixed layer heat and momentum interact with atmospheric boundary layer dynamics at mesoscale and higher resolution. This can capture the feedbacks which are otherwise not well-resolved in coarse resolution global coupled models and are absent in regional uncoupled models. To test the regional coupled model, we focus on a series of heat wave events that occurred on the eastern shore of the Red Sea region in June 2012 using a 30-day simulation. The results obtained using the coupled model, along with those in forced uncoupled ocean or atmosphere model simulations, are compared with observational and reanalysis data. All configurations of coupled and uncoupled models have good skill in modeling variables of interest in the region. The coupled model shows improved skill in temperature and circulation evaluation metrics. In addition, a scalability test is performed to investigate the parallelization of the coupled model. The results indicate that the coupled model scales linearly for up to 128 CPUs and sublinearly for more processors. In the coupled simulation, the ESMF/NUOPC interface also scales well and accounts for less than 10 % of the total computational resources compared with uncoupled models. Hence this newly developed regional model scales efficiently for a large number of processors and can be applied for high-resolution coupled regional modeling studies.

scholarly journals Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

2021 ◽  
Author(s):  
Philip E. Bett ◽  
Gill M. Martin ◽  
Nick Dunstone ◽  
Adam A. Scaife ◽  
Hazel E. Thornton ◽  
...  
Keyword(s):  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Asian Summer Monsoon ◽  
Yangtze River Basin ◽  
Early Summer ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
June July ◽  
The Impact

AbstractSeasonal forecasts for Yangtze River basin rainfall in June, May–June–July (MJJ), and June–July–August (JJA) 2020 are presented, based on the Met Office GloSea5 system. The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon (EASM) index, which is transformed into regional-mean rainfall through linear regression. The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation. The forecasts verify well in terms of giving strong, consistent predictions of above-average rainfall at lead times of at least three months. However, the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period, leading to observed values that lie outside the 95% prediction intervals of the three-month forecasts. The forecasts presented here are consistent with other studies of the 2020 EASM rainfall, whereby the enhanced mei-yu front in early summer is skillfully forecast, but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured. This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

scholarly journals Frequency-Domain Analysis of the Energy Budget in an Idealized Coupled Ocean–Atmosphere Model

2020 ◽  
Vol 33 (2) ◽  
pp. 707-726 ◽  
Author(s):  
Paige E. Martin ◽  
Brian K. Arbic ◽  
Andrew McC. Hogg ◽  
Andrew E. Kiss ◽  
James R. Munroe ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Time Scales ◽  
Energy Budget ◽  
Western Boundary Current ◽  
Spectral Energy ◽  
Western Boundary ◽  
Intrinsic Variability ◽  
Boundary Current ◽  
Ocean Atmosphere ◽  
Atmosphere Model

AbstractClimate variability is investigated by identifying the energy sources and sinks in an idealized, coupled, ocean–atmosphere model, tuned to mimic the North Atlantic region. The spectral energy budget is calculated in the frequency domain to determine the processes that either deposit energy into or extract energy from each fluid, over time scales from one day up to 100 years. Nonlinear advection of kinetic energy is found to be the dominant source of low-frequency variability in both the ocean and the atmosphere, albeit in differing layers in each fluid. To understand the spatial patterns of the spectral energy budget, spatial maps of certain terms in the spectral energy budget are plotted, averaged over various frequency bands. These maps reveal three dynamically distinct regions: along the western boundary, the western boundary current separation, and the remainder of the domain. The western boundary current separation is found to be a preferred region to energize oceanic variability across a broad range of time scales (from monthly to decadal), while the western boundary itself acts as the dominant sink of energy in the domain at time scales longer than 50 days. This study paves the way for future work, using the same spectral methods, to address the question of forced versus intrinsic variability in a coupled climate system.

Investigating the effects of a summer storm on the North Sea stratification using a regional coupled ocean-atmosphere model

2017 ◽  
Vol 67 (2) ◽  
pp. 211-235 ◽  
Author(s):  
Alexandra Gronholz ◽  
Ulf Gräwe ◽  
André Paul ◽  
Michael Schulz
Keyword(s):  
North Sea ◽  
The North ◽  
Ocean Atmosphere ◽  
The North Sea ◽  
Atmosphere Model ◽  
Summer Storm

Basin-Wide Connections of Upper-Ocean Temperature Variability in the Equatorial Indian Ocean

2021 ◽  
pp. 1-50
Author(s):  
Ge Song ◽  
Bohua Huang ◽  
Rongcai Ren ◽  
Zeng-Zhen Hu
Keyword(s):  
Indian Ocean ◽  
Equatorial Indian Ocean ◽  
Reanalysis Data ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Ocean Reanalysis ◽  
Near Surface ◽  
Surface Warming ◽  
Transition Mechanism ◽  
Wide Range

AbstractIn this paper, the interannual variability of upper-ocean temperature in the equatorial Indian Ocean (IO) and its basin-wide connections are investigated using 58-year (1958-2015) comprehensive monthly mean ocean reanalysis data. Three leading modes of an empirical orthogonal function (EOF) analysis dominate the variability of upper-ocean temperature in the equatorial IO in a wide range of timescales. A coherent interannual band within the first two EOF modes identifies an oscillation between the zonally tilting thermocline across the equatorial IO in its peak phases and basin-wide displacement of the equatorial thermocline in its transitional phases. Consistent with the recharge oscillation paradigm, this oscillation is inherent of the equatorial IO with a quasi-periodicity around 15 months, in which the wind-induced off-equatorial Rossby waves near 5°S-10°S provide the phase-transition mechanism. This intrinsic IO oscillation provides the biennial component in the observed IOD variations. The third leading mode shows a nonlinear long-term trend of the upper-ocean temperature, including the near-surface warming along the equatorial Indian Ocean, accompanied by cooling trend in the lower thermocline originating further south. Such vertical contrary trends may lead to an enhanced stratification in the equatorial IO.

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