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Method Developed To Predict Local Climate Change

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  Method Developed To Predict Local Climate Change Sam Wormley <swormley1@gmail.com> - 2016-02-22 10:55 -0600
    Re: Method Developed To Predict Local Climate Change jimp@specsol.spam.sux.com - 2016-02-22 19:28 +0000
    Re: Method Developed To Predict Local Climate Change Sergio <invalid@invalid.com> - 2016-02-22 15:01 -0600
    Re: Method Developed To Predict Local Climate Change James McGinn <jimmcginn9@gmail.com> - 2016-02-22 13:08 -0800
    Re: Method Developed To Predict Local Climate Change Double-A <double-a3@hush.com> - 2016-02-22 13:21 -0800
      Re: Method Developed To Predict Local Climate Change Sam Wormley <swormley1@gmail.com> - 2016-02-22 15:33 -0600

#555514 — Method Developed To Predict Local Climate Change

Method Developed To Predict Local Climate Change
> http://www.reportingclimatescience.com/news-stories/article/method-developed-to-predict-local-climate-change.html


> Abstract
>
> The mountain regions of the Northeastern US are a critical
> socioeconomic resource for Vermont, New York State, New Hampshire,
> Maine, and Southern Quebec. While global climate models (GCMs) are
> important tools for climate change risk assessment at regional
> scales, even the increased spatial resolution of statistically
> downscaled GCMs (commonly ~1/8°) is not sufficient for hydrologic,
> ecologic, and land-use modeling of small watersheds within the
> mountainous Northeast. To address this limitation, we develop an
> ensemble of topographically downscaled, high-resolution (30”), daily
> 2-m maximum air temperature, 2-m minimum air temperature, and
> precipitation simulations for the mountainous Northeast by applying
> an additional level of downscaling to intermediately downscaled
> (1/8°) data using high-resolution topography and station
> observations. We first derive observed relationships between 2-m air
> temperature and elevation, and precipitation and elevation. Then,
> these relationships are combined with spatial interpolation to
> enhance the resolution of intermediately downscaled GCM simulations.
> The resulting topographically downscaled dataset is analyzed for its
> ability to reproduce station observations. We find that topographic
> downscaling adds value to intermediately downscaled maximum and
> minimum 2-m air temperature at high elevation stations, as well as
> moderately improves domain-averaged maximum and minimum 2-m air
> temperature. Topographic downscaling improves mean precipitation but
> not daily probability distributions of precipitation. Overall, we
> show that the utility of topographic downscaling is dependent on the
> initial bias of the intermediately downscaled product and the
> magnitude of the elevation adjustment. As the initial bias or
> elevation adjustment increase, more value is added to the
> topographically downscaled product.
>
> Citation
>
> Development and Evaluation of High-Resolution Climate Simulations
> over the Mountainous Northeastern United States by Jonathan M.
> Winter, Brian Beckage, Gabriela Bucini, Radley M. Horton and Patrick
> J. Clemins published in Journal of Hydrometeorology, doi:
> 10.1175/JHM-D-15-0052.1


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#555579

Sam Wormley <swormley1@gmail.com> wrote:
> Method Developed To Predict Local Climate Change

And it will be just as inaccurate as all the other models.


-- 
Jim Pennino

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#555620

On 2/22/2016 10:55 AM, Sam Wormley wrote:
> Method Developed To Predict Local Climate Change

>>
>
>


too many inside buzzz words, consolidated into poopette;

ThemountainregionsoftheNortheasternUSareacriticalsocioeconomic
esourceforVermontNewYorkStateNewHampshireMaineandSouthernQuebe
Whileglobalclimatemodels(GCMs)areimportanttoolsforclimatechang
riskassessmentatregionalscaleseventheincreasedspatialresolutio
ofstatisticallydownscaledGCMs(commonly~1/8°)isnotsufficientfor
ydrologicecologicandlandusemodelingofsmallwatershedswithinthem
untainousNortheastToaddressthislimitationwedevelopanensembleof
opographicallydownscaledhighresolution(30”)daily2mmaximumairte
mperature2mminimumairtemperatureandprecipitationsimulationsfor
hemountainousNortheastbyapplyinganadditionallevelofdownscaling
tointermediatelydownscaled(1/8°)datausinghighresolutiontopogra
hyandstationobservationsWefirstderiveobservedrelationshipsbetw
een2mairtemperatureandelevationandprecipitationandelevationThe
theserelationshipsarecombinedwithspatialinterpolationtoenhance
theresolutionofintermediatelydownscaledGCMsimulationsTheresult
ingtopographicallydownscaleddatasetisanalyzedforitsabilitytore
producestationobservationsWefindthattopographicdownscalingadds
valuetointermediatelydownscaledmaximumandminimum2mairtemperatu
reathighelevationstationsaswellasmoderatelyimprovesdomainavera
gedmaximumandminimum2mairtemperatureTopographicdownscalingimpr
ovesmeanprecipitationbutnotdailyprobabilitydistributionsofprec
ipitationOverallweshowthattheutilityoftopographicdownscalingis
dependentontheinitialbiasoftheintermediatelydownscaledproducta
ndthemagnitudeoftheelevationadjustmentAstheinitialbiasorelevat
ionadjustmentincreasemorevalueisaddedtothetopographicallydowns

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#555625

Another layer of phoney complexity to confuse the public into thinking climatology is doing something useful.  

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#555626

On Monday, February 22, 2016 at 8:55:24 AM UTC-8, Sam Wormley wrote:
> Method Developed To Predict Local Climate Change
> > http://www.reportingclimatescience.com/news-stories/article/method-developed-to-predict-local-climate-change.html
> 
> 
> > Abstract
> >
> > The mountain regions of the Northeastern US are a critical
> > socioeconomic resource for Vermont, New York State, New Hampshire,
> > Maine, and Southern Quebec. While global climate models (GCMs) are
> > important tools for climate change risk assessment at regional
> > scales, even the increased spatial resolution of statistically
> > downscaled GCMs (commonly ~1/8°) is not sufficient for hydrologic,
> > ecologic, and land-use modeling of small watersheds within the
> > mountainous Northeast. To address this limitation, we develop an
> > ensemble of topographically downscaled, high-resolution (30"), daily
> > 2-m maximum air temperature, 2-m minimum air temperature, and
> > precipitation simulations for the mountainous Northeast by applying
> > an additional level of downscaling to intermediately downscaled
> > (1/8°) data using high-resolution topography and station
> > observations. We first derive observed relationships between 2-m air
> > temperature and elevation, and precipitation and elevation. Then,
> > these relationships are combined with spatial interpolation to
> > enhance the resolution of intermediately downscaled GCM simulations.
> > The resulting topographically downscaled dataset is analyzed for its
> > ability to reproduce station observations. We find that topographic
> > downscaling adds value to intermediately downscaled maximum and
> > minimum 2-m air temperature at high elevation stations, as well as
> > moderately improves domain-averaged maximum and minimum 2-m air
> > temperature. Topographic downscaling improves mean precipitation but
> > not daily probability distributions of precipitation. Overall, we
> > show that the utility of topographic downscaling is dependent on the
> > initial bias of the intermediately downscaled product and the
> > magnitude of the elevation adjustment. As the initial bias or
> > elevation adjustment increase, more value is added to the
> > topographically downscaled product.
> >
> > Citation
> >
> > Development and Evaluation of High-Resolution Climate Simulations
> > over the Mountainous Northeastern United States by Jonathan M.
> > Winter, Brian Beckage, Gabriela Bucini, Radley M. Horton and Patrick
> > J. Clemins published in Journal of Hydrometeorology, doi:
> > 10.1175/JHM-D-15-0052.1


None of the Republican candidates running for President believe in climate change!

Double-A

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#555628

On 2/22/16 3:21 PM, Double-A wrote:
> On Monday, February 22, 2016 at 8:55:24 AM UTC-8, Sam Wormley wrote:
>> Method Developed To Predict Local Climate Change
>>> http://www.reportingclimatescience.com/news-stories/article/method-developed-to-predict-local-climate-change.html
>>
>>
>>> Abstract
>>>
>>> The mountain regions of the Northeastern US are a critical
>>> socioeconomic resource for Vermont, New York State, New Hampshire,
>>> Maine, and Southern Quebec. While global climate models (GCMs) are
>>> important tools for climate change risk assessment at regional
>>> scales, even the increased spatial resolution of statistically
>>> downscaled GCMs (commonly ~1/8°) is not sufficient for hydrologic,
>>> ecologic, and land-use modeling of small watersheds within the
>>> mountainous Northeast. To address this limitation, we develop an
>>> ensemble of topographically downscaled, high-resolution (30"), daily
>>> 2-m maximum air temperature, 2-m minimum air temperature, and
>>> precipitation simulations for the mountainous Northeast by applying
>>> an additional level of downscaling to intermediately downscaled
>>> (1/8°) data using high-resolution topography and station
>>> observations. We first derive observed relationships between 2-m air
>>> temperature and elevation, and precipitation and elevation. Then,
>>> these relationships are combined with spatial interpolation to
>>> enhance the resolution of intermediately downscaled GCM simulations.
>>> The resulting topographically downscaled dataset is analyzed for its
>>> ability to reproduce station observations. We find that topographic
>>> downscaling adds value to intermediately downscaled maximum and
>>> minimum 2-m air temperature at high elevation stations, as well as
>>> moderately improves domain-averaged maximum and minimum 2-m air
>>> temperature. Topographic downscaling improves mean precipitation but
>>> not daily probability distributions of precipitation. Overall, we
>>> show that the utility of topographic downscaling is dependent on the
>>> initial bias of the intermediately downscaled product and the
>>> magnitude of the elevation adjustment. As the initial bias or
>>> elevation adjustment increase, more value is added to the
>>> topographically downscaled product.
>>>
>>> Citation
>>>
>>> Development and Evaluation of High-Resolution Climate Simulations
>>> over the Mountainous Northeastern United States by Jonathan M.
>>> Winter, Brian Beckage, Gabriela Bucini, Radley M. Horton and Patrick
>>> J. Clemins published in Journal of Hydrometeorology, doi:
>>> 10.1175/JHM-D-15-0052.1
>
>
> None of the Republican candidates running for President believe in climate change!
>
> Double-A
>


   Politicians--too bad none have science backgrounds.



-- 

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to the discussion of physics, news from the physics
community, and physics-related social issues.

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