Editing IPCC:AR6/Data/Glossary (section)

== K ==

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<div id="Kaya_identity"></div>
=== <span class="glossary-term">Kaya identity</span> ===

<div class="glossary-working-groups">'''Working Groups:''' WGIII</div>

<div class="glossary-definition">'''Definition:''' In this identity, global emissions are equal to the population size, multiplied by per capita output (gross world product), multiplied by the energy intensity of production, multiplied by the carbon intensity of energy.</div>
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<div id="Key_climate_indicators"></div>
=== <span class="glossary-term">Key climate indicators</span> ===

<div class="glossary-working-groups">'''Working Groups:''' WGI</div>

<div class="glossary-definition">'''Definition:''' Key indicators constitute a finite set of distinct variables that may collectively point to important overall changes in the climate system of broad societal relevance across the atmospheric, oceanic, cryospheric and biospheric domains, with land as an implicit cross-cutting theme. Taken together, these indicators would be expected to both have changed and continue to change in the future in a coherent and consistent manner. See Cross-Chapter Box 2.2, Table 1 in the AR6 WGI report.</div>
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<div class="glossary-entry">
<div id="Key_risk"></div>
=== <span class="glossary-term">Key risk</span> ===

<div class="glossary-working-groups">'''Working Groups:''' WGII</div>

<div class="glossary-definition">'''Definition:''' Key risks have potentially severe adverse consequences for humans and social-ecological systems resulting from the interaction of climate related hazards with vulnerabilities of societies and systems exposed.</div>
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<div class="glossary-entry">
<div id="Kriging"></div>
=== <span class="glossary-term">Kriging</span> ===

<div class="glossary-working-groups">'''Working Groups:''' WGI</div>

<div class="glossary-definition">'''Definition:''' Kriging is a method of interpolation (normally spatial interpolation when used with atmospheric or oceanographic data) in which the interpolated values are estimated using a Gaussian process governed by prior covariances.</div>
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<div class="glossary-letter-section">